diff --git a/examples/generalized_linear_models/GLM-discrete-choice_models.ipynb b/examples/generalized_linear_models/GLM-discrete-choice_models.ipynb
index 41931e47e..2d2e2edf9 100644
--- a/examples/generalized_linear_models/GLM-discrete-choice_models.ipynb
+++ b/examples/generalized_linear_models/GLM-discrete-choice_models.ipynb
@@ -196,14 +196,14 @@
     "\n",
     "$$ \\text{softmax}(u)_{j} = \\frac{\\exp(u_{j})}{\\sum_{q=1}^{J}\\exp(u_{q})} $$\n",
     "\n",
-    "The model then assumes that decision maker chooses the option that maximises their subjective utility. The individual utility functions can be richly parameterised. The model is identified just when the utility measures of the alternatives are benchmarked against the fixed utility of the \"outside good.\" The last quantity is fixed at 0. \n",
+    "The model then assumes that decision maker chooses the option that maximises their subjective utility. The individual utility functions can be richly parameterised. When contants are included, one alternative's constant must be fixed at 0 in order to normalise the overall level of the constants. \n",
     "\n",
     "$$\\begin{pmatrix}\n",
     "u_{gc}   \\\\\n",
     "u_{gr}   \\\\\n",
     "u_{ec}   \\\\\n",
     "u_{er}   \\\\\n",
-    "0   \\\\\n",
+    "u_{hp}   \\\\\n",
     "\\end{pmatrix}\n",
     "$$\n",
     "\n",
@@ -379,23 +379,16 @@
      "output_type": "stream",
      "text": [
       "Sampling: [beta_ic, beta_oc, y_cat]\n",
-      "/Users/nathanielforde/mambaforge/envs/pymc_examples_new/lib/python3.9/site-packages/pymc/sampling/mcmc.py:243: UserWarning: Use of external NUTS sampler is still experimental\n",
-      "  warnings.warn(\"Use of external NUTS sampler is still experimental\", UserWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
       "Compiling...\n",
-      "Compilation time =  0:00:01.199248\n",
+      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n",
+      "Compilation time = 0:00:01.806791\n",
       "Sampling...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "258e7c249c4d4353a285ee1640c3a58b",
+       "model_id": "a93fafe587e84dc0beb31d9222eca911",
        "version_major": 2,
        "version_minor": 0
       },
@@ -409,7 +402,7 @@
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-       "model_id": "9755fcd74e784c93a56d1563a6fd714c",
+       "model_id": "bddaf80409b24c6ca3ea21e8a0fa350e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -423,7 +416,7 @@
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "40d5abd188bd4f69af0e1b3602b3514b",
+       "model_id": "0c9e8311ef3145c4b5af75e49b96b924",
        "version_major": 2,
        "version_minor": 0
       },
@@ -437,7 +430,7 @@
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-       "model_id": "9aa8dd4eff7345449b2afb4924577136",
+       "model_id": "5fec714b5e8e47e4a9268f369fe90b33",
        "version_major": 2,
        "version_minor": 0
       },
@@ -449,93 +442,139 @@
      "output_type": "display_data"
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling time =  0:00:02.165671\n",
+      "Sampling time = 0:00:03.589848\n",
       "Transforming variables...\n",
-      "Transformation time =  0:00:00.404064\n",
-      "Computing Log Likelihood...\n"
+      "Transformation time = 0:00:00.487455\n",
+      "Computing Log Likelihood...\n",
+      "Log Likelihood time = 0:00:00.660260\n",
+      "Sampling: [y_cat]\n"
      ]
     },
+    {
+     "data": {
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-       "<!-- beta_oc&#45;&gt;p -->\n",
+       "<!-- beta_ic&#45;&gt;p -->\n",
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      "execution_count": 5,
@@ -560,7 +599,9 @@
     "    u1 = beta_ic * wide_heating_df[\"ic.er\"] + beta_oc * wide_heating_df[\"oc.er\"]\n",
     "    u2 = beta_ic * wide_heating_df[\"ic.gc\"] + beta_oc * wide_heating_df[\"oc.gc\"]\n",
     "    u3 = beta_ic * wide_heating_df[\"ic.gr\"] + beta_oc * wide_heating_df[\"oc.gr\"]\n",
-    "    u4 = np.zeros(N)  # Outside Good\n",
+    "    u4 = (\n",
+    "        beta_ic * wide_heating_df[\"ic.hp\"] + beta_oc * wide_heating_df[\"oc.hp\"]\n",
+    "    )  # np.zeros(N)  # Outside Good\n",
     "    s = pm.math.stack([u0, u1, u2, u3, u4]).T\n",
     "\n",
     "    ## Apply Softmax Transform\n",
@@ -594,8 +635,8 @@
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        "              \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterioraf2678bd-b557-49f6-9e74-7a8affc950d8\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_posterioraf2678bd-b557-49f6-9e74-7a8affc950d8\" class = \"xr-section-summary\">posterior</label>\n",
+       "                  <input id=\"idata_posteriorfc5ad570-35bd-4424-92e3-6d099eafd224\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posteriorfc5ad570-35bd-4424-92e3-6d099eafd224\" class = \"xr-section-summary\">posterior</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -970,79 +1011,79 @@
        "  * obs         (obs) int64 0 1 2 3 4 5 6 7 ... 892 893 894 895 896 897 898 899\n",
        "  * alts_probs  (alts_probs) &lt;U2 &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;\n",
        "Data variables:\n",
-       "    beta_ic     (chain, draw) float64 0.001854 0.001794 ... 0.002087 0.001928\n",
-       "    beta_oc     (chain, draw) float64 -0.003802 -0.003846 ... -0.00447 -0.004533\n",
-       "    p           (chain, draw, obs, alts_probs) float64 0.07322 0.1129 ... 0.1111\n",
+       "    beta_ic     (chain, draw) float64 -0.006063 -0.006218 ... -0.005608\n",
+       "    beta_oc     (chain, draw) float64 -0.004401 -0.00453 ... -0.004273 -0.004928\n",
+       "    p           (chain, draw, obs, alts_probs) float64 0.09993 ... 0.02927\n",
        "Attributes:\n",
-       "    created_at:     2023-07-13T17:29:12.988497\n",
-       "    arviz_version:  0.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6deaa8e6-55aa-4459-8259-26911bb41f13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6deaa8e6-55aa-4459-8259-26911bb41f13' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f53d7b95-a258-41de-adcc-4044e878ea08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f53d7b95-a258-41de-adcc-4044e878ea08' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-a695b37a-7f9b-4b8b-b80c-0d2e61c01314' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a695b37a-7f9b-4b8b-b80c-0d2e61c01314' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c96b4dcf-b67b-447d-823c-14df4a747a1a' class='xr-var-data-in' type='checkbox'><label for='data-c96b4dcf-b67b-447d-823c-14df4a747a1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-e4b88777-54ff-482e-abd3-735dd690080b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4b88777-54ff-482e-abd3-735dd690080b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-124a891e-d2d1-4f5a-a6f3-2111cc676b32' class='xr-var-data-in' type='checkbox'><label for='data-124a891e-d2d1-4f5a-a6f3-2111cc676b32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-39fad67f-9b3c-4af7-ad0d-7d6e611a7de1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39fad67f-9b3c-4af7-ad0d-7d6e611a7de1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a48ae244-5ecb-426f-850d-8e19d044907d' class='xr-var-data-in' type='checkbox'><label for='data-a48ae244-5ecb-426f-850d-8e19d044907d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-6af46398-6851-4516-b81c-78f4d3004c09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6af46398-6851-4516-b81c-78f4d3004c09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8b9efb2-cc16-4ce1-889b-bf41423421df' class='xr-var-data-in' type='checkbox'><label for='data-f8b9efb2-cc16-4ce1-889b-bf41423421df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17c680cf-3523-414b-9d55-e1f8264f532d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17c680cf-3523-414b-9d55-e1f8264f532d' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_ic</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001854 0.001794 ... 0.001928</div><input id='attrs-725cf2f8-7dc6-47b3-9849-cd7b8bdd2512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-725cf2f8-7dc6-47b3-9849-cd7b8bdd2512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2104eb43-5a1b-4395-945c-c92642c3a18b' class='xr-var-data-in' type='checkbox'><label for='data-2104eb43-5a1b-4395-945c-c92642c3a18b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00185432, 0.0017936 , 0.00168995, ..., 0.00179124, 0.0017465 ,\n",
-       "        0.00167666],\n",
-       "       [0.00177376, 0.00165435, 0.00215904, ..., 0.00198785, 0.00193043,\n",
-       "        0.00181715],\n",
-       "       [0.00185793, 0.00232339, 0.00194828, ..., 0.00203171, 0.0021245 ,\n",
-       "        0.00171454],\n",
-       "       [0.0018481 , 0.00185684, 0.00183757, ..., 0.00195163, 0.00208711,\n",
-       "        0.00192813]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_oc</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.003802 -0.003846 ... -0.004533</div><input id='attrs-11ddb143-f2b1-4782-9271-3d598b578bfe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11ddb143-f2b1-4782-9271-3d598b578bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e93aa7-2c81-4dea-aae0-44b7daca800f' class='xr-var-data-in' type='checkbox'><label for='data-85e93aa7-2c81-4dea-aae0-44b7daca800f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.00380192, -0.00384633, -0.00387485, ..., -0.00462905,\n",
-       "        -0.00462057, -0.00383906],\n",
-       "       [-0.00407646, -0.00419749, -0.00384315, ..., -0.00435413,\n",
-       "        -0.00422983, -0.00419225],\n",
-       "       [-0.00389835, -0.00446452, -0.00464509, ..., -0.00496631,\n",
-       "        -0.00477238, -0.00364794],\n",
-       "       [-0.00398744, -0.00407702, -0.00393378, ..., -0.00416899,\n",
-       "        -0.00447043, -0.00453276]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07322 0.1129 ... 0.4256 0.1111</div><input id='attrs-155fd7fa-2cf9-4bfd-a999-f74fd2ba8cb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-155fd7fa-2cf9-4bfd-a999-f74fd2ba8cb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40c80c52-6d65-41bd-8aa2-d16e8ddb912d' class='xr-var-data-in' type='checkbox'><label for='data-40c80c52-6d65-41bd-8aa2-d16e8ddb912d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[0.07322309, 0.11294964, 0.2841128 , 0.40787089, 0.12184357],\n",
-       "         [0.09165709, 0.1249422 , 0.30704356, 0.32518664, 0.1511705 ],\n",
-       "         [0.10218969, 0.16264231, 0.23121714, 0.36117826, 0.14277259],\n",
+       "    created_at:     2023-12-08T23:15:51.335704\n",
+       "    arviz_version:  0.16.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4256b237-fd16-4513-a5c0-5e1345943880' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4256b237-fd16-4513-a5c0-5e1345943880' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aff42f6f-fa68-42de-a33f-726ec4bee7fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aff42f6f-fa68-42de-a33f-726ec4bee7fe' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-aa6f14b2-77be-4ba7-8b46-9beeb585874d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa6f14b2-77be-4ba7-8b46-9beeb585874d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3161eb75-2e71-45e0-b471-e715d100f20a' class='xr-var-data-in' type='checkbox'><label for='data-3161eb75-2e71-45e0-b471-e715d100f20a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-a2724949-a935-4d67-bdb8-d4f9a13486ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2724949-a935-4d67-bdb8-d4f9a13486ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36303ac2-e078-4015-a2a5-e4ad80fb82c8' class='xr-var-data-in' type='checkbox'><label for='data-36303ac2-e078-4015-a2a5-e4ad80fb82c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-a8471bb6-2691-43b4-8c50-c658b63c19e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8471bb6-2691-43b4-8c50-c658b63c19e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e87c7032-0d32-4802-add1-7111482f6bb6' class='xr-var-data-in' type='checkbox'><label for='data-e87c7032-0d32-4802-add1-7111482f6bb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-351b91d3-e5f5-42fd-9961-3a7a38e6fa04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-351b91d3-e5f5-42fd-9961-3a7a38e6fa04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05e7b0de-b808-4e49-a7a7-5857ef0ed78c' class='xr-var-data-in' type='checkbox'><label for='data-05e7b0de-b808-4e49-a7a7-5857ef0ed78c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b335a26-9983-4a61-b14d-ce399a284766' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4b335a26-9983-4a61-b14d-ce399a284766' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_ic</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.006063 -0.006218 ... -0.005608</div><input id='attrs-f10c524f-5abf-4fa1-b526-a8e985f44de6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f10c524f-5abf-4fa1-b526-a8e985f44de6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71bf845d-1682-4f04-a931-6ddce5d79f0a' class='xr-var-data-in' type='checkbox'><label for='data-71bf845d-1682-4f04-a931-6ddce5d79f0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.00606349, -0.0062182 , -0.0062182 , ..., -0.0063582 ,\n",
+       "        -0.00591212, -0.00651122],\n",
+       "       [-0.00616057, -0.00572094, -0.00641705, ..., -0.00632234,\n",
+       "        -0.00637743, -0.00649658],\n",
+       "       [-0.00664726, -0.00588415, -0.00608324, ..., -0.00651181,\n",
+       "        -0.00634935, -0.00635049],\n",
+       "       [-0.00654706, -0.00587781, -0.00585245, ..., -0.00617327,\n",
+       "        -0.00664388, -0.00560836]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_oc</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.004401 -0.00453 ... -0.004928</div><input id='attrs-8d47b80a-c147-4113-951e-8830524df17a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d47b80a-c147-4113-951e-8830524df17a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71cece4f-2627-462e-a4fe-9e1d54256bd7' class='xr-var-data-in' type='checkbox'><label for='data-71cece4f-2627-462e-a4fe-9e1d54256bd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.00440139, -0.00452989, -0.00452989, ..., -0.00490241,\n",
+       "        -0.00467136, -0.00457889],\n",
+       "       [-0.00467581, -0.0042885 , -0.00490396, ..., -0.00475938,\n",
+       "        -0.00435274, -0.00463089],\n",
+       "       [-0.00431938, -0.0052031 , -0.00444931, ..., -0.00473675,\n",
+       "        -0.00465963, -0.00462437],\n",
+       "       [-0.00479991, -0.00435855, -0.00418114, ..., -0.00470016,\n",
+       "        -0.0042734 , -0.00492829]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09993 0.0541 ... 0.1809 0.02927</div><input id='attrs-b10b181c-688b-486a-870c-ef30789cfe90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b10b181c-688b-486a-870c-ef30789cfe90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba541bf-be70-450c-9590-a4787e3ed194' class='xr-var-data-in' type='checkbox'><label for='data-6ba541bf-be70-450c-9590-a4787e3ed194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[0.09993029, 0.0540987 , 0.45670526, 0.3139387 , 0.07532706],\n",
+       "         [0.06196248, 0.03971157, 0.44216555, 0.36648545, 0.08967495],\n",
+       "         [0.08838021, 0.03445866, 0.61110178, 0.2268888 , 0.03917056],\n",
        "         ...,\n",
-       "         [0.08249413, 0.13373172, 0.23616285, 0.44085863, 0.10675267],\n",
-       "         [0.06839877, 0.17788318, 0.22552951, 0.42617996, 0.10200859],\n",
-       "         [0.08482953, 0.13414628, 0.27236361, 0.40738278, 0.1012778 ]],\n",
+       "         [0.11632761, 0.03988059, 0.62096406, 0.14156331, 0.08126442],\n",
+       "         [0.21328011, 0.04531231, 0.51524693, 0.17575438, 0.05040627],\n",
+       "         [0.09325094, 0.05735483, 0.6603553 , 0.16463034, 0.02440859]],\n",
        "\n",
-       "        [[0.07191539, 0.11025465, 0.28335124, 0.40526019, 0.12921853],\n",
-       "         [0.08962555, 0.12163123, 0.30624134, 0.32372789, 0.15877399],\n",
-       "         [0.10060054, 0.15861184, 0.23209332, 0.35897101, 0.14972329],\n",
+       "        [[0.09690334, 0.05168491, 0.46302318, 0.31549684, 0.07289173],\n",
+       "         [0.05926717, 0.03757593, 0.44721737, 0.36890133, 0.0870382 ],\n",
+       "         [0.08506459, 0.03239634, 0.62066483, 0.22478761, 0.03708663],\n",
        "         ...,\n",
-       "         [0.08155359, 0.13079867, 0.23744346, 0.43673993, 0.11346434],\n",
-       "         [0.06799272, 0.174111  , 0.2262014 , 0.42287908, 0.1088158 ],\n",
-       "         [0.08351165, 0.1314237 , 0.27347175, 0.40338936, 0.10820353]],\n",
+       "         [0.11290717, 0.0376834 , 0.63212196, 0.13884678, 0.07844069],\n",
+       "         [0.21096954, 0.04314976, 0.5236688 , 0.17395966, 0.04825224],\n",
+       "         [0.08969548, 0.05454113, 0.67142447, 0.16154967, 0.02278924]],\n",
        "\n",
-       "        [[0.0707887 , 0.10715561, 0.28156113, 0.39923231, 0.14126225],\n",
-       "         [0.08744061, 0.11758153, 0.30394884, 0.32027513, 0.17075389],\n",
-       "         [0.09898362, 0.15332404, 0.23304048, 0.35392342, 0.16072844],\n",
+       "        [[0.09690334, 0.05168491, 0.46302318, 0.31549684, 0.07289173],\n",
+       "         [0.05926717, 0.03757593, 0.44721737, 0.36890133, 0.0870382 ],\n",
+       "         [0.08506459, 0.03239634, 0.62066483, 0.22478761, 0.03708663],\n",
        "         ...,\n",
        "...\n",
        "         ...,\n",
-       "         [0.07434766, 0.12424822, 0.23666514, 0.45907092, 0.10566806],\n",
-       "         [0.06105228, 0.16958401, 0.22476353, 0.4435739 , 0.10102629],\n",
-       "         [0.07642407, 0.12504988, 0.27629164, 0.42177755, 0.10045687]],\n",
+       "         [0.10795027, 0.03653951, 0.63486677, 0.14194505, 0.0786984 ],\n",
+       "         [0.20194651, 0.04258246, 0.52756113, 0.17880878, 0.04910112],\n",
+       "         [0.08532874, 0.05271242, 0.67489238, 0.16392071, 0.02314574]],\n",
        "\n",
-       "        [[0.05882476, 0.0966925 , 0.28953088, 0.43895928, 0.11599257],\n",
-       "         [0.07701313, 0.10985028, 0.3211502 , 0.34258706, 0.14939933],\n",
-       "         [0.08840643, 0.15014045, 0.23352096, 0.38783924, 0.14009291],\n",
+       "        [[0.10777963, 0.05359538, 0.46911594, 0.30301435, 0.06649469],\n",
+       "         [0.0635307 , 0.03830245, 0.45108845, 0.36722282, 0.07985558],\n",
+       "         [0.08893233, 0.03109288, 0.636737  , 0.21176012, 0.03147767],\n",
        "         ...,\n",
-       "         [0.06805284, 0.11789883, 0.2356187 , 0.4787461 , 0.09968353],\n",
-       "         [0.05517125, 0.164697  , 0.22310792, 0.46191585, 0.095108  ],\n",
-       "         [0.07017311, 0.11890053, 0.2785132 , 0.43781697, 0.0945962 ]],\n",
+       "         [0.1214392 , 0.03695891, 0.64400993, 0.1251241 , 0.07246785],\n",
+       "         [0.23408116, 0.04072864, 0.52639397, 0.1567621 , 0.04203413],\n",
+       "         [0.09625973, 0.05467735, 0.68100058, 0.14899203, 0.01907031]],\n",
        "\n",
-       "        [[0.05711837, 0.09215532, 0.28711905, 0.42994953, 0.13365773],\n",
-       "         [0.0737155 , 0.10374125, 0.31766931, 0.33720999, 0.16766396],\n",
-       "         [0.08587985, 0.14203293, 0.23520541, 0.38005886, 0.15682296],\n",
+       "        [[0.08204068, 0.04845037, 0.45300152, 0.3337238 , 0.08278364],\n",
+       "         [0.05303494, 0.0361758 , 0.44142172, 0.37106076, 0.09830678],\n",
+       "         [0.07886095, 0.03398496, 0.59622061, 0.24420104, 0.04673243],\n",
        "         ...,\n",
-       "         [0.06705557, 0.11277456, 0.238708  , 0.46575398, 0.11570788],\n",
-       "         [0.05516607, 0.15734329, 0.22488893, 0.45122393, 0.11137777],\n",
-       "         [0.06842681, 0.11410865, 0.28072918, 0.42561475, 0.11112061]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a0c4d3d-51f4-440f-a23d-624906c383e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-2a0c4d3d-51f4-440f-a23d-624906c383e2' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6af11aa-3ece-4157-8e6c-781b7eed29a3' class='xr-index-data-in' type='checkbox'/><label for='index-d6af11aa-3ece-4157-8e6c-781b7eed29a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b96556ed-3857-4d53-b568-f79fdeb0e41b' class='xr-index-data-in' type='checkbox'/><label for='index-b96556ed-3857-4d53-b568-f79fdeb0e41b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1aa54da5-6ecc-49c7-b818-5fd24d6dc835' class='xr-index-data-in' type='checkbox'/><label for='index-1aa54da5-6ecc-49c7-b818-5fd24d6dc835' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a21306f-9e09-458c-a782-5b66deda806c' class='xr-index-data-in' type='checkbox'/><label for='index-5a21306f-9e09-458c-a782-5b66deda806c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0bef50cb-56a6-4c35-9a99-11e75252d1d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0bef50cb-56a6-4c35-9a99-11e75252d1d4' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:12.988497</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "         [0.10039966, 0.03827986, 0.61323832, 0.16075082, 0.08733134],\n",
+       "         [0.17882432, 0.0462435 , 0.51586216, 0.20078143, 0.05828859],\n",
+       "         [0.07998719, 0.05372487, 0.65613223, 0.18088401, 0.02927169]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5cf02b5-dc4a-4e5e-bd9d-73446a7d95cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-d5cf02b5-dc4a-4e5e-bd9d-73446a7d95cd' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f518f89f-55bc-4783-811a-dc46cdaa641d' class='xr-index-data-in' type='checkbox'/><label for='index-f518f89f-55bc-4783-811a-dc46cdaa641d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-733df05f-eded-4461-aed8-40208527ace1' class='xr-index-data-in' type='checkbox'/><label for='index-733df05f-eded-4461-aed8-40208527ace1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee7a4d7c-4a1d-4ae5-96b2-199f2b4f23be' class='xr-index-data-in' type='checkbox'/><label for='index-ee7a4d7c-4a1d-4ae5-96b2-199f2b4f23be' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba41c37f-b6c1-4a2b-b563-f260e50c3953' class='xr-index-data-in' type='checkbox'/><label for='index-ba41c37f-b6c1-4a2b-b563-f260e50c3953' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8df28b53-c183-42e6-968b-916f8e5089f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8df28b53-c183-42e6-968b-916f8e5089f4' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:15:51.335704</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterior_predictive9e6db604-96a2-45b4-82aa-078a8a7849d9\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_posterior_predictive9e6db604-96a2-45b4-82aa-078a8a7849d9\" class = \"xr-section-summary\">posterior_predictive</label>\n",
+       "                  <input id=\"idata_posterior_predictive604ee123-9730-44bc-b6e9-0248c60b97f7\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior_predictive604ee123-9730-44bc-b6e9-0248c60b97f7\" class = \"xr-section-summary\">posterior_predictive</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -1416,55 +1457,55 @@
        "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
        "  * obs      (obs) int64 0 1 2 3 4 5 6 7 8 ... 892 893 894 895 896 897 898 899\n",
        "Data variables:\n",
-       "    y_cat    (chain, draw, obs) int64 4 3 4 2 2 1 1 2 4 1 ... 4 3 2 1 4 2 1 1 0\n",
+       "    y_cat    (chain, draw, obs) int64 4 2 2 3 3 3 3 3 4 2 ... 2 0 3 0 2 2 2 2 2\n",
        "Attributes:\n",
-       "    created_at:                 2023-07-13T17:29:19.656809\n",
-       "    arviz_version:              0.15.1\n",
+       "    created_at:                 2023-12-08T23:16:02.315123\n",
+       "    arviz_version:              0.16.1\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-27dc23c6-fbe0-4693-aaf8-a8c208f8ebc8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-27dc23c6-fbe0-4693-aaf8-a8c208f8ebc8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-942d96da-59d7-4415-a0ad-017c95c581e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-942d96da-59d7-4415-a0ad-017c95c581e8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-82d28f8f-fce6-47db-b928-9fa780188194' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82d28f8f-fce6-47db-b928-9fa780188194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a7f1f5-9c13-4f64-aa04-d3ac471608e2' class='xr-var-data-in' type='checkbox'><label for='data-e1a7f1f5-9c13-4f64-aa04-d3ac471608e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-3d8aa9ba-6718-44d6-a86f-795a07764f23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d8aa9ba-6718-44d6-a86f-795a07764f23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf8dc01b-34eb-41b1-90eb-2e7fa9d41c47' class='xr-var-data-in' type='checkbox'><label for='data-cf8dc01b-34eb-41b1-90eb-2e7fa9d41c47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-025c52e8-3614-4845-a6fe-10405a8e3fa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-025c52e8-3614-4845-a6fe-10405a8e3fa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa638a7c-d77c-4842-9bed-4deb47a53d63' class='xr-var-data-in' type='checkbox'><label for='data-fa638a7c-d77c-4842-9bed-4deb47a53d63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa29d599-24aa-4521-9c5d-0a592c9caa84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa29d599-24aa-4521-9c5d-0a592c9caa84' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4 3 4 2 2 1 1 2 ... 3 2 1 4 2 1 1 0</div><input id='attrs-8ca79434-b666-463f-b02d-b7684fce706d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ca79434-b666-463f-b02d-b7684fce706d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8b40863-f757-42b8-82ee-67ada7dd76df' class='xr-var-data-in' type='checkbox'><label for='data-f8b40863-f757-42b8-82ee-67ada7dd76df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[4, 3, 4, ..., 0, 4, 3],\n",
-       "        [3, 1, 3, ..., 3, 3, 1],\n",
-       "        [0, 4, 1, ..., 3, 2, 1],\n",
+       "    inference_library_version:  5.9.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-687e6383-2391-4f63-80e5-a0674f1d5908' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-687e6383-2391-4f63-80e5-a0674f1d5908' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b7c7ee86-8a6a-4b08-9dd8-7b50a3ff646c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7c7ee86-8a6a-4b08-9dd8-7b50a3ff646c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-605b6f79-a4cf-43b4-965e-1bd16d9b4f4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-605b6f79-a4cf-43b4-965e-1bd16d9b4f4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51093c1-fc92-498d-886d-c55e44a03ebd' class='xr-var-data-in' type='checkbox'><label for='data-b51093c1-fc92-498d-886d-c55e44a03ebd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-2fafd2a2-3d00-4ec3-8782-620bbdd6b1e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fafd2a2-3d00-4ec3-8782-620bbdd6b1e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54621e90-6e7e-4b8e-8176-351216749832' class='xr-var-data-in' type='checkbox'><label for='data-54621e90-6e7e-4b8e-8176-351216749832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-40648fce-77a5-406f-86a0-90719492844b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40648fce-77a5-406f-86a0-90719492844b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce06b1b4-fb65-42a6-ad10-431cd5b2f8a9' class='xr-var-data-in' type='checkbox'><label for='data-ce06b1b4-fb65-42a6-ad10-431cd5b2f8a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-258cc960-309d-4670-a35e-2e5c44387669' class='xr-section-summary-in' type='checkbox'  checked><label for='section-258cc960-309d-4670-a35e-2e5c44387669' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4 2 2 3 3 3 3 3 ... 0 3 0 2 2 2 2 2</div><input id='attrs-ed6e89ef-3ba5-441e-91c4-ea14d50a3ee2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed6e89ef-3ba5-441e-91c4-ea14d50a3ee2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa26761b-082e-4eb4-bcc0-e76482a63c75' class='xr-var-data-in' type='checkbox'><label for='data-aa26761b-082e-4eb4-bcc0-e76482a63c75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[4, 2, 2, ..., 2, 0, 2],\n",
+       "        [1, 3, 3, ..., 2, 0, 0],\n",
+       "        [0, 3, 2, ..., 2, 3, 2],\n",
        "        ...,\n",
-       "        [3, 3, 4, ..., 3, 2, 2],\n",
-       "        [3, 3, 4, ..., 4, 2, 4],\n",
-       "        [0, 2, 2, ..., 3, 0, 2]],\n",
+       "        [4, 1, 2, ..., 0, 0, 2],\n",
+       "        [2, 3, 3, ..., 3, 0, 2],\n",
+       "        [3, 2, 2, ..., 2, 2, 3]],\n",
        "\n",
-       "       [[2, 2, 3, ..., 3, 3, 2],\n",
-       "        [4, 0, 1, ..., 3, 4, 1],\n",
-       "        [2, 3, 3, ..., 3, 0, 3],\n",
+       "       [[2, 2, 2, ..., 2, 2, 2],\n",
+       "        [3, 2, 2, ..., 0, 3, 1],\n",
+       "        [3, 2, 0, ..., 0, 0, 0],\n",
        "        ...,\n",
-       "        [3, 2, 1, ..., 2, 2, 3],\n",
-       "        [3, 3, 0, ..., 1, 3, 4],\n",
-       "        [2, 3, 2, ..., 3, 3, 3]],\n",
+       "        [2, 2, 2, ..., 0, 2, 0],\n",
+       "        [0, 3, 2, ..., 0, 3, 2],\n",
+       "        [2, 1, 3, ..., 2, 3, 2]],\n",
        "\n",
-       "       [[0, 2, 3, ..., 3, 2, 1],\n",
-       "        [3, 2, 3, ..., 3, 3, 3],\n",
-       "        [3, 1, 3, ..., 2, 3, 4],\n",
+       "       [[0, 2, 2, ..., 0, 3, 2],\n",
+       "        [1, 0, 2, ..., 2, 0, 2],\n",
+       "        [2, 3, 2, ..., 2, 3, 3],\n",
        "        ...,\n",
-       "        [2, 4, 4, ..., 3, 1, 3],\n",
-       "        [3, 2, 2, ..., 1, 3, 3],\n",
-       "        [1, 4, 2, ..., 2, 2, 3]],\n",
+       "        [4, 2, 3, ..., 3, 2, 2],\n",
+       "        [3, 2, 2, ..., 2, 3, 1],\n",
+       "        [0, 3, 2, ..., 0, 0, 2]],\n",
        "\n",
-       "       [[3, 2, 2, ..., 3, 1, 1],\n",
-       "        [3, 2, 2, ..., 2, 3, 3],\n",
-       "        [0, 2, 1, ..., 4, 3, 2],\n",
+       "       [[2, 3, 2, ..., 4, 2, 2],\n",
+       "        [4, 2, 0, ..., 2, 0, 3],\n",
+       "        [2, 3, 3, ..., 2, 2, 0],\n",
        "        ...,\n",
-       "        [3, 2, 3, ..., 3, 1, 3],\n",
-       "        [3, 2, 1, ..., 1, 1, 2],\n",
-       "        [3, 2, 0, ..., 1, 1, 0]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7ac356a-26f0-4836-8d41-30f0e3907712' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7ac356a-26f0-4836-8d41-30f0e3907712' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5892931b-7f0e-4e1b-8b4b-1779b76579be' class='xr-index-data-in' type='checkbox'/><label for='index-5892931b-7f0e-4e1b-8b4b-1779b76579be' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-04ac2b3e-e066-4a05-ad6d-7034897adc49' class='xr-index-data-in' type='checkbox'/><label for='index-04ac2b3e-e066-4a05-ad6d-7034897adc49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-15b2ad9c-21ee-4259-b210-44aa2caf14c1' class='xr-index-data-in' type='checkbox'/><label for='index-15b2ad9c-21ee-4259-b210-44aa2caf14c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c64f940f-a4ef-4e79-9fd0-ce4cd81761e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c64f940f-a4ef-4e79-9fd0-ce4cd81761e6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:19.656809</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.3.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "        [2, 3, 2, ..., 3, 2, 2],\n",
+       "        [3, 3, 2, ..., 2, 0, 3],\n",
+       "        [2, 2, 2, ..., 2, 2, 2]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6cc924b5-d703-4fb5-89da-4311e4e7bc0c' class='xr-section-summary-in' type='checkbox'  ><label for='section-6cc924b5-d703-4fb5-89da-4311e4e7bc0c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0442ec45-e97a-4bcd-9bcc-b353964280b0' class='xr-index-data-in' type='checkbox'/><label for='index-0442ec45-e97a-4bcd-9bcc-b353964280b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b82d613c-4378-440e-bae3-d55af3290027' class='xr-index-data-in' type='checkbox'/><label for='index-b82d613c-4378-440e-bae3-d55af3290027' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed99ad0c-2796-4b14-932b-27a1f15f4bcf' class='xr-index-data-in' type='checkbox'/><label for='index-ed99ad0c-2796-4b14-932b-27a1f15f4bcf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e794f573-5bae-4590-9fc6-e4e0a75f622d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e794f573-5bae-4590-9fc6-e4e0a75f622d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:16:02.315123</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.9.2</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_log_likelihoodeac55502-e01b-4eae-839f-1fe44f488796\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_log_likelihoodeac55502-e01b-4eae-839f-1fe44f488796\" class = \"xr-section-summary\">log_likelihood</label>\n",
+       "                  <input id=\"idata_log_likelihoodcb8e1503-e628-486b-aec7-670c1a03fe8d\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_log_likelihoodcb8e1503-e628-486b-aec7-670c1a03fe8d\" class = \"xr-section-summary\">log_likelihood</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -1839,63 +1880,63 @@
        "  * y_cat_dim_0  (y_cat_dim_0) int64 0\n",
        "  * y_cat_dim_1  (y_cat_dim_1) int64 0 1 2 3 4 5 6 ... 894 895 896 897 898 899\n",
        "Data variables:\n",
-       "    y_cat        (chain, draw, y_cat_dim_0, y_cat_dim_1) float64 -1.258 ... -...\n",
+       "    y_cat        (chain, draw, y_cat_dim_0, y_cat_dim_1) float64 -0.7837 ... ...\n",
        "Attributes:\n",
-       "    created_at:     2023-07-13T17:29:12.992158\n",
-       "    arviz_version:  0.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ea89cf0d-d0f5-49a3-b92c-17e9c74c61fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ea89cf0d-d0f5-49a3-b92c-17e9c74c61fc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>y_cat_dim_0</span>: 1</li><li><span class='xr-has-index'>y_cat_dim_1</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f8d1c9ce-7acd-43e4-a81d-e6e391b0080e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f8d1c9ce-7acd-43e4-a81d-e6e391b0080e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-fd96a2a2-d6e3-405a-adc6-f36ad8ba3416' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd96a2a2-d6e3-405a-adc6-f36ad8ba3416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb93ecb9-0f0c-4aff-9c9e-94bbdb6bf5b8' class='xr-var-data-in' type='checkbox'><label for='data-cb93ecb9-0f0c-4aff-9c9e-94bbdb6bf5b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-e75d597e-6934-40f5-bf14-d6d25dd3a2c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e75d597e-6934-40f5-bf14-d6d25dd3a2c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-604beb3c-c0f1-4b7b-bf6c-2f6a95f1f6f8' class='xr-var-data-in' type='checkbox'><label for='data-604beb3c-c0f1-4b7b-bf6c-2f6a95f1f6f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_cat_dim_0</span></div><div class='xr-var-dims'>(y_cat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-76eb7836-dd7b-4346-8800-c9cbd71bdfc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76eb7836-dd7b-4346-8800-c9cbd71bdfc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b89da21f-d79c-4d2c-aec2-9672e7efaa8e' class='xr-var-data-in' type='checkbox'><label for='data-b89da21f-d79c-4d2c-aec2-9672e7efaa8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_cat_dim_1</span></div><div class='xr-var-dims'>(y_cat_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-30abbd10-731d-4d7b-809b-002e36eefdd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30abbd10-731d-4d7b-809b-002e36eefdd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca5126b2-0d7e-487e-a18e-a4480e52eea0' class='xr-var-data-in' type='checkbox'><label for='data-ca5126b2-0d7e-487e-a18e-a4480e52eea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5280e059-5215-4cd6-a8a6-45126f377392' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5280e059-5215-4cd6-a8a6-45126f377392' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, y_cat_dim_0, y_cat_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.258 -1.181 ... -1.492 -1.27</div><input id='attrs-bedb0696-67ed-492a-bb42-c26c7b1d021e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bedb0696-67ed-492a-bb42-c26c7b1d021e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7d73283-e7c9-4ba9-a8f4-9f8644fe9b9a' class='xr-var-data-in' type='checkbox'><label for='data-d7d73283-e7c9-4ba9-a8f4-9f8644fe9b9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-1.25838394, -1.18076564, -1.46439801, ..., -1.44323366,\n",
-       "          -1.48930428, -1.30061729]],\n",
+       "    created_at:     2023-12-08T23:15:51.341719\n",
+       "    arviz_version:  0.16.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5896bbf2-8581-4e63-8904-9a5dc5913124' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5896bbf2-8581-4e63-8904-9a5dc5913124' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>y_cat_dim_0</span>: 1</li><li><span class='xr-has-index'>y_cat_dim_1</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8a78541a-5b2c-4c36-b1a9-c3f22d7f7259' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a78541a-5b2c-4c36-b1a9-c3f22d7f7259' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-7b3f79d7-24f4-4f1c-b1b5-2c3374ed86f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b3f79d7-24f4-4f1c-b1b5-2c3374ed86f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c8d6910-c408-4b76-8266-d44d6b55f9e1' class='xr-var-data-in' type='checkbox'><label for='data-6c8d6910-c408-4b76-8266-d44d6b55f9e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-9e18b036-54c5-445f-bede-86ee674c8ed1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e18b036-54c5-445f-bede-86ee674c8ed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3f504be-f9ed-4bc2-9f72-833430266a77' class='xr-var-data-in' type='checkbox'><label for='data-b3f504be-f9ed-4bc2-9f72-833430266a77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_cat_dim_0</span></div><div class='xr-var-dims'>(y_cat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-5960d8a4-b3a4-4388-b26d-13e8b2b1dcbf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5960d8a4-b3a4-4388-b26d-13e8b2b1dcbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c74acead-9327-4fa2-adbb-ec015c03dbd2' class='xr-var-data-in' type='checkbox'><label for='data-c74acead-9327-4fa2-adbb-ec015c03dbd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_cat_dim_1</span></div><div class='xr-var-dims'>(y_cat_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-b1ef7885-19a2-4582-8259-54291ceb6458' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1ef7885-19a2-4582-8259-54291ceb6458' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f61a52d-5aee-4d4b-bf9c-4e9eef6eaf79' class='xr-var-data-in' type='checkbox'><label for='data-0f61a52d-5aee-4d4b-bf9c-4e9eef6eaf79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5fa2a77-800f-4458-9060-7dead406e192' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5fa2a77-800f-4458-9060-7dead406e192' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, y_cat_dim_0, y_cat_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.7837 -0.8161 ... -0.6619 -0.4214</div><input id='attrs-d50306cb-5581-4f37-a62d-5abb57a9a52e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d50306cb-5581-4f37-a62d-5abb57a9a52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d890be0a-01cd-4ab3-b847-90052129098f' class='xr-var-data-in' type='checkbox'><label for='data-d890be0a-01cd-4ab3-b847-90052129098f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[-0.78371704, -0.81607092, -0.49249175, ..., -0.47648207,\n",
+       "          -0.66310902, -0.41497726]],\n",
        "\n",
-       "        [[-1.26106802, -1.18338178, -1.46061574, ..., -1.43782573,\n",
-       "          -1.48632955, -1.29655694]],\n",
+       "        [[-0.76997815, -0.80471051, -0.47696406, ..., -0.45867293,\n",
+       "          -0.64689586, -0.39835374]],\n",
        "\n",
-       "        [[-1.26740569, -1.19089588, -1.45654309, ..., -1.43093889,\n",
-       "          -1.48261589, -1.29312192]],\n",
+       "        [[-0.76997815, -0.80471051, -0.47696406, ..., -0.45867293,\n",
+       "          -0.64689586, -0.39835374]],\n",
        "\n",
        "        ...,\n",
        "\n",
-       "        [[-1.25775021, -1.15734211, -1.44256548, ..., -1.4233743 ,\n",
-       "          -1.48837697, -1.26490478]],\n",
+       "        [[-0.74915922, -0.78636832, -0.45751072, ..., -0.43198285,\n",
+       "          -0.61805035, -0.37231103]],\n",
        "\n",
-       "        [[-1.261385  , -1.16229154, -1.44212494, ..., -1.42140372,\n",
-       "          -1.48738218, -1.26506468]],\n",
+       "        [[-0.78234255, -0.8130019 , -0.49788198, ..., -0.47573174,\n",
+       "          -0.65622294, -0.41202084]],\n",
        "\n",
-       "        [[-1.26872681, -1.19349149, -1.45717302, ..., -1.43107286,\n",
-       "          -1.4822969 , -1.29456549]]],\n",
+       "        [[-0.75190526, -0.79070509, -0.45351152, ..., -0.43506626,\n",
+       "          -0.62869245, -0.37745601]]],\n",
        "\n",
        "...\n",
        "\n",
-       "       [[[-1.25609642, -1.17308582, -1.45835841, ..., -1.43792089,\n",
-       "          -1.48815798, -1.29134221]],\n",
+       "       [[[-0.7421076 , -0.78191873, -0.44575988, ..., -0.42283665,\n",
+       "          -0.61385135, -0.36520607]],\n",
        "\n",
-       "        [[-1.2546239 , -1.16876045, -1.45611314, ..., -1.43640874,\n",
-       "          -1.48834488, -1.28746152]],\n",
+       "        [[-0.79612414, -0.82592215, -0.50877761, ..., -0.49311718,\n",
+       "          -0.67629348, -0.42993177]],\n",
        "\n",
-       "        [[-1.25735608, -1.17616661, -1.45956454, ..., -1.43852903,\n",
-       "          -1.48786646, -1.29357521]],\n",
+       "        [[-0.8050047 , -0.83400061, -0.51583324, ..., -0.50399957,\n",
+       "          -0.68864736, -0.44111659]],\n",
        "\n",
        "        ...,\n",
        "\n",
-       "        [[-1.24850717, -1.15764379, -1.45708094, ..., -1.44110905,\n",
-       "          -1.49270641, -1.28629831]],\n",
+       "        [[-0.76632855, -0.80072477, -0.47631793, ..., -0.45434011,\n",
+       "          -0.63949053, -0.39320204]],\n",
        "\n",
-       "        [[-1.23949333, -1.13584634, -1.45448342, ..., -1.44554047,\n",
-       "          -1.5000997 , -1.27828983]],\n",
+       "        [[-0.75690533, -0.79609184, -0.45139858, ..., -0.44004113,\n",
+       "          -0.64170536, -0.38419212]],\n",
        "\n",
-       "        [[-1.24785833, -1.14674436, -1.44729605, ..., -1.43251422,\n",
-       "          -1.49214862, -1.27036485]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-edc71d00-b8c4-4544-9ef3-7490168b212d' class='xr-section-summary-in' type='checkbox'  ><label for='section-edc71d00-b8c4-4544-9ef3-7490168b212d' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a1657ca-60d0-4e64-9dca-dfb216820a6d' class='xr-index-data-in' type='checkbox'/><label for='index-1a1657ca-60d0-4e64-9dca-dfb216820a6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-449383e9-3a9b-4f6d-b0bb-7e59174e8c21' class='xr-index-data-in' type='checkbox'/><label for='index-449383e9-3a9b-4f6d-b0bb-7e59174e8c21' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_cat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-056a8a00-ae76-4207-a64f-f648a4e717da' class='xr-index-data-in' type='checkbox'/><label for='index-056a8a00-ae76-4207-a64f-f648a4e717da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0], dtype=&#x27;int64&#x27;, name=&#x27;y_cat_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_cat_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64b174b9-7759-492e-958f-168379ffcd59' class='xr-index-data-in' type='checkbox'/><label for='index-64b174b9-7759-492e-958f-168379ffcd59' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;y_cat_dim_1&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49cc0909-5220-4d64-8a8d-ee7e09697ebf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49cc0909-5220-4d64-8a8d-ee7e09697ebf' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:12.992158</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "        [[-0.7918598 , -0.81775458, -0.51714452, ..., -0.48900165,\n",
+       "          -0.66191567, -0.42139293]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3034de5a-1b83-4f3f-8cf0-a5d11751bdc6' class='xr-section-summary-in' type='checkbox'  ><label for='section-3034de5a-1b83-4f3f-8cf0-a5d11751bdc6' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce87a62b-76c8-4521-b50b-b60f30d2245d' class='xr-index-data-in' type='checkbox'/><label for='index-ce87a62b-76c8-4521-b50b-b60f30d2245d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d8f5823-afa7-404d-900e-645d35b21656' class='xr-index-data-in' type='checkbox'/><label for='index-2d8f5823-afa7-404d-900e-645d35b21656' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_cat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9172c324-1c0a-4793-87df-c4f1e35dd911' class='xr-index-data-in' type='checkbox'/><label for='index-9172c324-1c0a-4793-87df-c4f1e35dd911' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;y_cat_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_cat_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-99beaa70-b1ce-4004-91cb-a647f0594f63' class='xr-index-data-in' type='checkbox'/><label for='index-99beaa70-b1ce-4004-91cb-a647f0594f63' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y_cat_dim_1&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-864f9c9f-fc4e-4a9f-8c9d-1eb01dcbd3e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-864f9c9f-fc4e-4a9f-8c9d-1eb01dcbd3e3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:15:51.341719</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_sample_stats92068fa1-3fb3-4017-830e-3793612ab198\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_sample_stats92068fa1-3fb3-4017-830e-3793612ab198\" class = \"xr-section-summary\">sample_stats</label>\n",
+       "                  <input id=\"idata_sample_stats1e822054-4d0a-4411-bbcd-f706a3a75acf\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_sample_stats1e822054-4d0a-4411-bbcd-f706a3a75acf\" class = \"xr-section-summary\">sample_stats</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -2268,63 +2309,63 @@
        "  * chain            (chain) int64 0 1 2 3\n",
        "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
        "Data variables:\n",
-       "    acceptance_rate  (chain, draw) float64 0.9667 1.0 0.8868 ... 0.8931 0.9283\n",
-       "    step_size        (chain, draw) float64 0.02965 0.02965 ... 0.03209 0.03209\n",
+       "    acceptance_rate  (chain, draw) float64 0.9776 1.0 0.9124 ... 0.7537 0.9359\n",
+       "    step_size        (chain, draw) float64 0.08947 0.08947 ... 0.09163 0.09163\n",
        "    diverging        (chain, draw) bool False False False ... False False False\n",
-       "    energy           (chain, draw) float64 1.311e+03 1.311e+03 ... 1.312e+03\n",
-       "    n_steps          (chain, draw) int64 3 3 3 3 1 11 3 7 3 ... 7 7 7 11 7 3 7 3\n",
-       "    tree_depth       (chain, draw) int64 2 2 2 2 1 4 2 3 2 ... 2 3 3 3 4 3 2 3 2\n",
-       "    lp               (chain, draw) float64 1.311e+03 1.311e+03 ... 1.311e+03\n",
+       "    energy           (chain, draw) float64 1.098e+03 1.097e+03 ... 1.099e+03\n",
+       "    n_steps          (chain, draw) int64 3 3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3 3\n",
+       "    tree_depth       (chain, draw) int64 2 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2 2\n",
+       "    lp               (chain, draw) float64 1.097e+03 1.097e+03 ... 1.099e+03\n",
        "Attributes:\n",
-       "    created_at:     2023-07-13T17:29:12.990891\n",
-       "    arviz_version:  0.15.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-857a1ac4-72b2-427b-b73a-3399dc03909d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-857a1ac4-72b2-427b-b73a-3399dc03909d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0db6d57b-15de-4f73-8d83-6bd44135df95' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0db6d57b-15de-4f73-8d83-6bd44135df95' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-bd5a7f6b-cb29-4aa3-b47b-90fa23b33955' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd5a7f6b-cb29-4aa3-b47b-90fa23b33955' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43150eae-686f-4a91-91fd-85955b9eb6d4' class='xr-var-data-in' type='checkbox'><label for='data-43150eae-686f-4a91-91fd-85955b9eb6d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-3a7f5342-f0e5-4208-a41d-51b704ced80d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a7f5342-f0e5-4208-a41d-51b704ced80d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a787a7a8-1b7e-4539-8d67-5e6d64ca9fda' class='xr-var-data-in' type='checkbox'><label for='data-a787a7a8-1b7e-4539-8d67-5e6d64ca9fda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d252ead2-75e8-4e94-ae79-b744c44e7a9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d252ead2-75e8-4e94-ae79-b744c44e7a9b' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9667 1.0 0.8868 ... 0.8931 0.9283</div><input id='attrs-6100cc1b-b9c4-4a75-87b8-feef4e83fa72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6100cc1b-b9c4-4a75-87b8-feef4e83fa72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28e5b1ff-cbf6-4949-972c-fbf8fe88c299' class='xr-var-data-in' type='checkbox'><label for='data-28e5b1ff-cbf6-4949-972c-fbf8fe88c299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.96668742, 1.        , 0.88678803, ..., 0.832775  , 0.80147234,\n",
-       "        0.97320939],\n",
-       "       [0.91898504, 0.73812295, 0.65193552, ..., 1.        , 0.99555924,\n",
-       "        0.95500129],\n",
-       "       [0.97029319, 0.5078157 , 1.        , ..., 0.97650608, 1.        ,\n",
+       "    created_at:     2023-12-08T23:15:51.339556\n",
+       "    arviz_version:  0.16.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a079e72d-a2ae-413f-947a-3eb5004232f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a079e72d-a2ae-413f-947a-3eb5004232f5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1c62ed54-afec-4295-a39b-c8a5a5c2b604' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c62ed54-afec-4295-a39b-c8a5a5c2b604' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-b851005e-0b32-484a-a840-4a0fa57c342c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b851005e-0b32-484a-a840-4a0fa57c342c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bee5bfb-7bfb-4fa4-ae72-c44c453d5f76' class='xr-var-data-in' type='checkbox'><label for='data-7bee5bfb-7bfb-4fa4-ae72-c44c453d5f76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-6e874204-e55d-44c5-9077-156bef26efdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e874204-e55d-44c5-9077-156bef26efdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-499e9091-16e0-4d2a-ab98-75cae4f88cc5' class='xr-var-data-in' type='checkbox'><label for='data-499e9091-16e0-4d2a-ab98-75cae4f88cc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35ffe0ce-f6a2-4f7e-946d-dfa107d3eeae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35ffe0ce-f6a2-4f7e-946d-dfa107d3eeae' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9776 1.0 0.9124 ... 0.7537 0.9359</div><input id='attrs-79ce3fd4-8c9d-4bd5-9e21-80ca7f30700f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79ce3fd4-8c9d-4bd5-9e21-80ca7f30700f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3768e315-3a06-4676-b73a-ad35c93a3619' class='xr-var-data-in' type='checkbox'><label for='data-3768e315-3a06-4676-b73a-ad35c93a3619' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.97763155, 1.        , 0.9123895 , ..., 0.91189046, 0.91446395,\n",
+       "        0.90135924],\n",
+       "       [0.99442479, 0.71224438, 0.8207381 , ..., 0.99968662, 0.90191326,\n",
+       "        0.99317112],\n",
+       "       [0.92062169, 0.43898892, 1.        , ..., 0.96899969, 0.99934639,\n",
        "        1.        ],\n",
-       "       [1.        , 0.98428559, 0.94659264, ..., 0.87880027, 0.89313587,\n",
-       "        0.92827725]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02965 0.02965 ... 0.03209 0.03209</div><input id='attrs-28a685ed-0d04-45c8-b155-bce86064f16f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28a685ed-0d04-45c8-b155-bce86064f16f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4746ca9c-65f5-4c46-b4d9-52199e4e7cb5' class='xr-var-data-in' type='checkbox'><label for='data-4746ca9c-65f5-4c46-b4d9-52199e4e7cb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.02965364, 0.02965364, 0.02965364, ..., 0.02965364, 0.02965364,\n",
-       "        0.02965364],\n",
-       "       [0.0348108 , 0.0348108 , 0.0348108 , ..., 0.0348108 , 0.0348108 ,\n",
-       "        0.0348108 ],\n",
-       "       [0.0335617 , 0.0335617 , 0.0335617 , ..., 0.0335617 , 0.0335617 ,\n",
-       "        0.0335617 ],\n",
-       "       [0.03209221, 0.03209221, 0.03209221, ..., 0.03209221, 0.03209221,\n",
-       "        0.03209221]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b2537166-2874-47a7-834a-a6995d75fc6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2537166-2874-47a7-834a-a6995d75fc6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c646e33-e8ae-42b6-88ac-b4a12252401e' class='xr-var-data-in' type='checkbox'><label for='data-9c646e33-e8ae-42b6-88ac-b4a12252401e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "       [0.99019186, 0.99612574, 0.86321359, ..., 0.93359717, 0.75367063,\n",
+       "        0.93588978]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08947 0.08947 ... 0.09163 0.09163</div><input id='attrs-9fd17867-d97b-49ab-85a1-202e4adb0917' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fd17867-d97b-49ab-85a1-202e4adb0917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4abb9ace-5ad5-4d97-b11f-c54afff2c486' class='xr-var-data-in' type='checkbox'><label for='data-4abb9ace-5ad5-4d97-b11f-c54afff2c486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.08947388, 0.08947388, 0.08947388, ..., 0.08947388, 0.08947388,\n",
+       "        0.08947388],\n",
+       "       [0.09549277, 0.09549277, 0.09549277, ..., 0.09549277, 0.09549277,\n",
+       "        0.09549277],\n",
+       "       [0.10774106, 0.10774106, 0.10774106, ..., 0.10774106, 0.10774106,\n",
+       "        0.10774106],\n",
+       "       [0.09163428, 0.09163428, 0.09163428, ..., 0.09163428, 0.09163428,\n",
+       "        0.09163428]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-7708294c-487c-4373-a67b-cbca31b1555b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7708294c-487c-4373-a67b-cbca31b1555b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eac84c8d-4291-4438-bc67-3009f54b1e34' class='xr-var-data-in' type='checkbox'><label for='data-eac84c8d-4291-4438-bc67-3009f54b1e34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.311e+03 1.311e+03 ... 1.312e+03</div><input id='attrs-e3f9fce2-c2ff-46be-abcf-c6c78cc08903' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3f9fce2-c2ff-46be-abcf-c6c78cc08903' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11b0a22-b10c-45d6-8e33-05b31ee81c73' class='xr-var-data-in' type='checkbox'><label for='data-e11b0a22-b10c-45d6-8e33-05b31ee81c73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1311.02807455, 1310.85624244, 1311.29145993, ..., 1313.34508594,\n",
-       "        1314.36026935, 1314.50846674],\n",
-       "       [1310.3936618 , 1312.79557002, 1315.79936109, ..., 1310.19152711,\n",
-       "        1310.69088735, 1310.18922683],\n",
-       "       [1310.66343379, 1316.9728028 , 1314.59817151, ..., 1313.57708469,\n",
-       "        1313.3000676 , 1311.96972255],\n",
-       "       [1310.81727096, 1310.15989558, 1310.50270126, ..., 1312.29193502,\n",
-       "        1312.1408833 , 1311.77428022]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 3 3 1 11 3 7 ... 7 7 11 7 3 7 3</div><input id='attrs-44e48382-0f7c-4ca4-8c1a-9a4506fa409e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44e48382-0f7c-4ca4-8c1a-9a4506fa409e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf7f28f1-6cad-45b2-a4e2-1cd3dab16ff7' class='xr-var-data-in' type='checkbox'><label for='data-cf7f28f1-6cad-45b2-a4e2-1cd3dab16ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3,  3,  3, ...,  3,  1,  9],\n",
-       "       [ 3,  3,  3, ...,  3,  7,  5],\n",
-       "       [ 7, 11,  3, ...,  3,  3, 15],\n",
-       "       [ 3,  3,  3, ...,  3,  7,  3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 2 1 4 2 3 ... 3 3 3 4 3 2 3 2</div><input id='attrs-4a5e11f5-b06f-49e4-b1d8-3568017a8317' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a5e11f5-b06f-49e4-b1d8-3568017a8317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73738f6f-da08-4688-b359-bc0faa629f4d' class='xr-var-data-in' type='checkbox'><label for='data-73738f6f-da08-4688-b359-bc0faa629f4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 2, ..., 2, 1, 4],\n",
-       "       [2, 2, 2, ..., 2, 3, 3],\n",
-       "       [3, 4, 2, ..., 2, 2, 4],\n",
-       "       [2, 2, 2, ..., 2, 3, 2]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.311e+03 1.311e+03 ... 1.311e+03</div><input id='attrs-a32269ba-4b19-4eab-94f3-49604712047e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a32269ba-4b19-4eab-94f3-49604712047e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff79cf3d-7f3e-43b0-b5cb-8f7695518ffc' class='xr-var-data-in' type='checkbox'><label for='data-ff79cf3d-7f3e-43b0-b5cb-8f7695518ffc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1310.85296473, 1310.5090792 , 1311.05196078, ..., 1313.07351323,\n",
-       "        1313.96754755, 1311.23235692],\n",
-       "       [1310.26682798, 1312.48985299, 1315.52133628, ..., 1310.07833465,\n",
-       "        1309.85428101, 1310.09987862],\n",
-       "       [1310.37326154, 1315.2401948 , 1311.20326201, ..., 1313.38619846,\n",
-       "        1311.99334014, 1311.60532063],\n",
-       "       [1310.05439245, 1309.89541318, 1310.19960031, ..., 1309.97580287,\n",
-       "        1310.84659945, 1310.64836248]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac0816d4-2f11-4777-95b8-fad426c9bf79' class='xr-section-summary-in' type='checkbox'  ><label for='section-ac0816d4-2f11-4777-95b8-fad426c9bf79' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2bf0eed2-5b14-4249-ac44-97b77834de37' class='xr-index-data-in' type='checkbox'/><label for='index-2bf0eed2-5b14-4249-ac44-97b77834de37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fad1fbd2-5e81-4a5c-8c54-239c28beee92' class='xr-index-data-in' type='checkbox'/><label for='index-fad1fbd2-5e81-4a5c-8c54-239c28beee92' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16c22efb-e400-450c-954a-6afe372fe41f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16c22efb-e400-450c-954a-6afe372fe41f' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:12.990891</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.098e+03 1.097e+03 ... 1.099e+03</div><input id='attrs-53f9ef82-d48a-4e1f-903f-fd74fdb3695e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53f9ef82-d48a-4e1f-903f-fd74fdb3695e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6468952a-a73b-44a5-815e-0297bd35d783' class='xr-var-data-in' type='checkbox'><label for='data-6468952a-a73b-44a5-815e-0297bd35d783' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1097.90950349, 1097.36422046, 1097.76408102, ..., 1100.39503349,\n",
+       "        1098.66672608, 1098.67424013],\n",
+       "       [1098.15388719, 1099.27908376, 1101.08526536, ..., 1097.31659306,\n",
+       "        1097.91532156, 1097.65291876],\n",
+       "       [1098.06289565, 1103.8600668 , 1099.18136448, ..., 1097.52624159,\n",
+       "        1097.43840975, 1097.17355288],\n",
+       "       [1097.82568096, 1097.8691726 , 1098.61066406, ..., 1100.02872094,\n",
+       "        1099.40014771, 1099.31391116]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3</div><input id='attrs-4f3ff096-2270-492c-82eb-a9c4a2f8cfc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f3ff096-2270-492c-82eb-a9c4a2f8cfc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9662d9b9-b53d-4275-9c30-92a5af772348' class='xr-var-data-in' type='checkbox'><label for='data-9662d9b9-b53d-4275-9c30-92a5af772348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 3, ..., 3, 7, 7],\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
+       "       [7, 3, 3, ..., 3, 3, 1],\n",
+       "       [3, 7, 1, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2</div><input id='attrs-e1eed6ae-f946-4c4d-9c29-3a23f06224ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1eed6ae-f946-4c4d-9c29-3a23f06224ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6a5e7c-e8f1-4a80-993e-950267b33b30' class='xr-var-data-in' type='checkbox'><label for='data-8f6a5e7c-e8f1-4a80-993e-950267b33b30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 2, ..., 2, 3, 3],\n",
+       "       [2, 2, 2, ..., 2, 2, 2],\n",
+       "       [3, 2, 2, ..., 2, 2, 1],\n",
+       "       [2, 3, 1, ..., 2, 2, 2]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.097e+03 1.097e+03 ... 1.099e+03</div><input id='attrs-0a516afa-213f-45f5-bbe6-8869651e496b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a516afa-213f-45f5-bbe6-8869651e496b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aa95bdb-c0d8-4a06-8657-caad45ba8363' class='xr-var-data-in' type='checkbox'><label for='data-8aa95bdb-c0d8-4a06-8657-caad45ba8363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1097.36089423, 1097.08832319, 1097.08832319, ..., 1097.64865711,\n",
+       "        1097.51831863, 1097.38555111],\n",
+       "       [1097.13634355, 1098.64597557, 1097.73482937, ..., 1097.26878686,\n",
+       "        1097.39788158, 1097.37262558],\n",
+       "       [1098.04342905, 1099.26550485, 1097.25738844, ..., 1097.52383524,\n",
+       "        1097.16505449, 1097.14329955],\n",
+       "       [1097.7304434 , 1097.86798173, 1098.53144277, ..., 1097.15469481,\n",
+       "        1098.15374095, 1099.14610513]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b4c22fa-9fa0-48b4-b197-2284b56dac69' class='xr-section-summary-in' type='checkbox'  ><label for='section-3b4c22fa-9fa0-48b4-b197-2284b56dac69' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c3a57f09-8cc9-4483-98fb-b6c2db9822a4' class='xr-index-data-in' type='checkbox'/><label for='index-c3a57f09-8cc9-4483-98fb-b6c2db9822a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-509350ef-666b-451e-ba8b-921926b368dc' class='xr-index-data-in' type='checkbox'/><label for='index-509350ef-666b-451e-ba8b-921926b368dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2cef2868-9bd1-4cc6-90f9-f896c4e12879' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cef2868-9bd1-4cc6-90f9-f896c4e12879' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:15:51.339556</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_priorb07a270d-69f6-4b9c-8de4-539b95140db4\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_priorb07a270d-69f6-4b9c-8de4-539b95140db4\" class = \"xr-section-summary\">prior</label>\n",
+       "                  <input id=\"idata_prior0e1bc1b8-0824-477c-927b-5223bbcb4328\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_prior0e1bc1b8-0824-477c-927b-5223bbcb4328\" class = \"xr-section-summary\">prior</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -2699,147 +2740,147 @@
        "  * obs         (obs) int64 0 1 2 3 4 5 6 7 ... 892 893 894 895 896 897 898 899\n",
        "  * alts_probs  (alts_probs) &lt;U2 &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;\n",
        "Data variables:\n",
-       "    beta_oc     (chain, draw) float64 0.05221 0.1678 0.7865 ... 0.5232 -0.09242\n",
-       "    p           (chain, draw, obs, alts_probs) float64 4.778e-201 ... 4.831e-91\n",
-       "    beta_ic     (chain, draw) float64 -0.57 -0.8283 0.2129 ... 1.097 0.2006\n",
+       "    beta_ic     (chain, draw) float64 -1.975 -1.131 1.304 ... 1.736 -1.448\n",
+       "    p           (chain, draw, obs, alts_probs) float64 1.526e-134 ... 0.0\n",
+       "    beta_oc     (chain, draw) float64 -0.9053 0.7179 -0.3289 ... 0.2239 -2.288\n",
        "Attributes:\n",
-       "    created_at:                 2023-07-13T17:29:07.842667\n",
-       "    arviz_version:              0.15.1\n",
+       "    created_at:                 2023-12-08T23:15:43.538814\n",
+       "    arviz_version:              0.16.1\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b37bcac5-6aea-4b40-8ddb-e117e5e78b4d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b37bcac5-6aea-4b40-8ddb-e117e5e78b4d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6d1a4388-8e6c-47b0-9168-fc54ffa512ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d1a4388-8e6c-47b0-9168-fc54ffa512ed' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-937bd293-897c-490c-ba1e-ede05fef766d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-937bd293-897c-490c-ba1e-ede05fef766d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9127f570-ef2f-4fe8-b4b6-5b630dbf6005' class='xr-var-data-in' type='checkbox'><label for='data-9127f570-ef2f-4fe8-b4b6-5b630dbf6005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-2651106a-bd92-4354-b3c2-90df88dacaac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2651106a-bd92-4354-b3c2-90df88dacaac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a144b3d-2fe8-463a-875e-5fa6954344cc' class='xr-var-data-in' type='checkbox'><label for='data-4a144b3d-2fe8-463a-875e-5fa6954344cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-27c9b8ed-012b-4e32-9255-9cd2e19e784a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27c9b8ed-012b-4e32-9255-9cd2e19e784a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9007fdfd-c930-4bc4-94ae-1debeae6a15b' class='xr-var-data-in' type='checkbox'><label for='data-9007fdfd-c930-4bc4-94ae-1debeae6a15b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-8ea60c4a-15da-4ef5-a5fd-51a5a46d8177' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ea60c4a-15da-4ef5-a5fd-51a5a46d8177' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e894d389-0fbf-412b-869a-c33fa045dfc3' class='xr-var-data-in' type='checkbox'><label for='data-e894d389-0fbf-412b-869a-c33fa045dfc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c8640a1-654d-42f6-b661-f88fe906af86' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c8640a1-654d-42f6-b661-f88fe906af86' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_oc</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05221 0.1678 ... 0.5232 -0.09242</div><input id='attrs-19b5f2db-34e5-4ebe-ba3c-1a7a4840cfff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19b5f2db-34e5-4ebe-ba3c-1a7a4840cfff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76a7548e-a97f-43a6-b24c-80cf43b5f2c9' class='xr-var-data-in' type='checkbox'><label for='data-76a7548e-a97f-43a6-b24c-80cf43b5f2c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 5.22123131e-02,  1.67808803e-01,  7.86499718e-01,\n",
-       "         6.78099653e-01, -9.53864839e-02,  5.76929240e-01,\n",
-       "         2.66210247e-01,  2.76490816e-01, -8.62046317e-01,\n",
-       "         1.43800720e-01,  6.05778337e-01, -5.01770894e-01,\n",
-       "        -1.01903815e+00,  1.44285382e+00,  1.44191513e+00,\n",
-       "        -1.72887211e+00,  2.58057973e-02, -1.40841177e+00,\n",
-       "         1.49409275e-01, -1.25941954e+00,  6.03880072e-01,\n",
-       "         6.44329176e-03,  1.55331894e+00,  1.19140854e+00,\n",
-       "         1.19125366e+00,  8.95483429e-02, -4.88048425e-01,\n",
-       "         2.38948238e-01,  6.13491798e-02, -1.05372724e-01,\n",
-       "         6.14166519e-01, -7.98300206e-01, -1.57627791e+00,\n",
-       "        -6.56281227e-01, -3.31887590e-01, -9.76469578e-02,\n",
-       "         3.73667319e-02, -4.46401625e-01, -6.32421376e-01,\n",
-       "        -5.15888903e-02,  4.91398108e-01,  2.65630805e-01,\n",
-       "        -3.36713237e-01, -6.06621909e-01,  4.64579807e-01,\n",
-       "         1.22095804e+00,  1.41716046e+00,  1.49022768e+00,\n",
-       "        -1.46466745e-02,  3.49249386e-01, -2.01548779e+00,\n",
-       "         1.37776656e+00,  5.88994694e-01,  1.13044210e+00,\n",
-       "        -4.99040316e-02, -1.07193117e+00, -9.94516858e-01,\n",
-       "        -1.26196021e+00, -1.28986812e+00, -1.91050443e+00,\n",
+       "    inference_library_version:  5.9.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1699809e-b7b9-46df-9cb1-24004f6029f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1699809e-b7b9-46df-9cb1-24004f6029f9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>obs</span>: 900</li><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9180179d-f9c8-4e14-8c05-e25fdc10986d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9180179d-f9c8-4e14-8c05-e25fdc10986d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-453afec4-0034-4987-98f8-3bdca2edf6f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-453afec4-0034-4987-98f8-3bdca2edf6f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8f71858-b168-443c-a6e5-65d4d9f93b29' class='xr-var-data-in' type='checkbox'><label for='data-d8f71858-b168-443c-a6e5-65d4d9f93b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-a9787a82-5484-4341-a5ca-58822121b5de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9787a82-5484-4341-a5ca-58822121b5de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d493ae-a010-4d19-9d1b-0158b3e48520' class='xr-var-data-in' type='checkbox'><label for='data-e6d493ae-a010-4d19-9d1b-0158b3e48520' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-d6fb7d3f-3dd3-4acd-a542-c1e2adcf7e9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6fb7d3f-3dd3-4acd-a542-c1e2adcf7e9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-682c9724-5a88-4640-9b49-76a91015fae3' class='xr-var-data-in' type='checkbox'><label for='data-682c9724-5a88-4640-9b49-76a91015fae3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-2a1c527e-73ac-4d0a-8aaa-98d4d420b652' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a1c527e-73ac-4d0a-8aaa-98d4d420b652' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24a2509d-6fdf-4e31-b30f-c9e7d37e142e' class='xr-var-data-in' type='checkbox'><label for='data-24a2509d-6fdf-4e31-b30f-c9e7d37e142e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31267047-8683-47dd-af98-1e955063a901' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31267047-8683-47dd-af98-1e955063a901' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beta_ic</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.975 -1.131 ... 1.736 -1.448</div><input id='attrs-123f8299-7531-48dc-904f-562ec543dda7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-123f8299-7531-48dc-904f-562ec543dda7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-767092aa-4d5a-4bf6-b93a-0102d629ca21' class='xr-var-data-in' type='checkbox'><label for='data-767092aa-4d5a-4bf6-b93a-0102d629ca21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.97549148e+00, -1.13116850e+00,  1.30423587e+00,\n",
+       "         7.74157553e-01, -6.40735081e-01, -2.68792461e+00,\n",
+       "        -1.43496587e+00,  5.88786311e-02, -1.87653900e+00,\n",
+       "         2.80644898e-01,  1.51910853e+00, -4.92379270e-01,\n",
+       "         6.85352397e-01,  9.34462279e-01, -1.43265148e+00,\n",
+       "        -4.71420406e-01, -2.09516481e-01, -1.34147189e-01,\n",
+       "        -8.59084853e-01, -1.71451570e-01, -1.07955723e+00,\n",
+       "        -9.95927046e-01, -5.73024543e-02, -9.46072320e-01,\n",
+       "         4.44836517e-02, -7.84430238e-02, -2.89755644e-01,\n",
+       "        -3.17253607e-01,  4.92868672e-01, -9.76835746e-02,\n",
+       "        -3.84529476e-01,  4.04279309e-01,  1.19084435e+00,\n",
+       "         4.46631189e-01,  6.99210502e-01, -1.82426077e+00,\n",
+       "        -5.86759347e-01, -4.45986137e-01,  5.69045022e-02,\n",
+       "        -3.24371782e-01, -9.18450471e-01,  2.70790447e-02,\n",
+       "         4.85978555e-01,  4.76921937e-01, -2.84202234e+00,\n",
+       "         1.58174771e+00,  1.11304477e+00, -9.76229026e-01,\n",
+       "         3.72329266e-01,  2.09050205e+00, -8.28767125e-01,\n",
+       "         2.51424898e+00,  3.10444225e-01,  7.07298639e-01,\n",
+       "         4.21564359e-03,  6.70121725e-01, -1.60818063e+00,\n",
+       "        -9.30110111e-01,  5.82690095e-01,  1.76438932e+00,\n",
        "...\n",
-       "         2.19951294e+00, -1.22471025e+00,  8.12392922e-01,\n",
-       "        -8.76963801e-01, -6.68999739e-01, -3.79367158e-01,\n",
-       "         3.21759821e-01, -8.71934942e-01, -7.71975654e-01,\n",
-       "         2.20439637e-01, -1.40235273e-02,  6.37145014e-02,\n",
-       "        -7.07404789e-02,  6.94661764e-01, -7.08752140e-01,\n",
-       "        -7.49656515e-01,  7.63016827e-02, -1.96157335e+00,\n",
-       "         4.29880784e-01,  2.26642863e-01, -1.33725618e+00,\n",
-       "        -1.33565769e+00, -6.20769298e-02, -1.56438832e+00,\n",
-       "         1.01849028e+00, -4.47346052e-01, -9.22840328e-01,\n",
-       "         1.52578096e+00, -1.01157454e+00,  5.01524450e-01,\n",
-       "        -1.80982817e-02, -2.56621616e-01, -1.49459668e-01,\n",
-       "         4.00916871e-03,  1.72359937e+00, -9.75697269e-01,\n",
-       "         1.15033052e+00, -7.77945279e-01,  6.28044749e-01,\n",
-       "         1.94636242e-01, -8.22248275e-01, -3.02361708e-01,\n",
-       "        -5.24404285e-01, -8.16118103e-01,  3.66017430e-01,\n",
-       "         3.54466167e-01,  1.39027437e+00, -3.03677763e-02,\n",
-       "        -1.36836882e+00,  1.59254769e+00, -1.10448020e+00,\n",
-       "         1.51725654e+00,  1.32489420e+00,  5.48989024e-01,\n",
-       "        -9.51623984e-01,  1.55167656e+00, -5.33382582e-01,\n",
-       "         5.23223875e-01, -9.24236936e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.778e-201 9.217e-236 ... 4.831e-91</div><input id='attrs-287d1cb1-44c1-4f82-be73-a547fe55f772' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-287d1cb1-44c1-4f82-be73-a547fe55f772' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc92b322-16b9-419a-b57a-c65a54065233' class='xr-var-data-in' type='checkbox'><label for='data-cc92b322-16b9-419a-b57a-c65a54065233' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[4.77797773e-201, 9.21735126e-236, 1.41251546e-210,\n",
-       "          1.37733934e-235, 1.00000000e+000],\n",
-       "         [3.50043885e-186, 3.55737733e-211, 4.22245926e-177,\n",
-       "          9.15008836e-185, 1.00000000e+000],\n",
-       "         [5.68528693e-169, 2.26822738e-214, 2.25678364e-145,\n",
-       "          1.90922389e-191, 1.00000000e+000],\n",
+       "        -9.17031539e-04, -4.39531140e-01,  2.67926631e-01,\n",
+       "         1.18467510e+00,  2.68929982e-01, -4.88868795e-01,\n",
+       "         5.50592581e-01,  5.84125722e-01, -1.94503540e+00,\n",
+       "        -2.02005191e-01, -8.22435811e-01, -7.07623643e-01,\n",
+       "        -8.29493823e-02,  2.37666865e-01, -1.06135213e-01,\n",
+       "         6.49028323e-01,  3.77446994e-01,  5.55662339e-01,\n",
+       "        -8.84469770e-01, -1.24718118e+00,  5.63293131e-01,\n",
+       "         1.11741762e+00,  6.11025088e-01, -7.92376341e-01,\n",
+       "        -3.56120555e-01,  3.91852672e-01, -4.70091455e-01,\n",
+       "        -4.89217649e-01, -1.63969705e+00,  1.93083777e+00,\n",
+       "         1.31555913e+00,  3.96646484e-01, -1.73187939e+00,\n",
+       "         1.31205629e+00, -1.18111283e+00,  1.74484840e+00,\n",
+       "         7.08674896e-01,  3.56009117e-01,  7.30872557e-01,\n",
+       "         7.57258950e-01,  1.77160303e-01, -1.40799426e+00,\n",
+       "         1.00513303e+00,  2.28772816e-01,  1.29778504e-01,\n",
+       "         2.34080546e-01,  1.07472775e+00, -8.36056248e-02,\n",
+       "        -8.38420950e-01, -7.08259111e-02, -1.40991666e-01,\n",
+       "        -5.37360540e-02, -1.30000518e+00,  9.79949498e-01,\n",
+       "         4.86498552e-02,  7.73316483e-01,  8.94253990e-01,\n",
+       "         1.73620339e+00, -1.44838217e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw, obs, alts_probs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.526e-134 2.477e-232 ... 0.0</div><input id='attrs-487b6f57-4605-4f35-8d70-fc4f7e065179' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-487b6f57-4605-4f35-8d70-fc4f7e065179' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1812a59a-e0f3-435d-9b8d-6f67897a2f71' class='xr-var-data-in' type='checkbox'><label for='data-1812a59a-e0f3-435d-9b8d-6f67897a2f71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1.52648875e-134, 2.47694805e-232, 1.00000000e+000,\n",
+       "          8.89734955e-065, 5.86252851e-247],\n",
+       "         [4.57389943e-198, 9.18730925e-269, 1.00000000e+000,\n",
+       "          2.74170823e-027, 1.80739096e-219],\n",
+       "         [1.55747642e-211, 0.00000000e+000, 1.00000000e+000,\n",
+       "          2.68966505e-147, 0.00000000e+000],\n",
        "         ...,\n",
-       "         [1.68247109e-198, 2.50560604e-248, 1.32722173e-191,\n",
-       "          9.93208296e-259, 1.00000000e+000],\n",
-       "         [1.77227549e-187, 1.14210303e-269, 5.67585094e-209,\n",
-       "          5.69496368e-265, 1.00000000e+000],\n",
-       "         [1.45508677e-228, 1.40393393e-260, 4.91739513e-218,\n",
-       "          7.13059332e-274, 1.00000000e+000]],\n",
-       "\n",
-       "        [[1.02747845e-269, 4.59481051e-322, 1.10683126e-297,\n",
-       "          0.00000000e+000, 1.00000000e+000],\n",
-       "         [1.94990948e-249, 4.22213523e-287, 2.80445009e-250,\n",
-       "          2.04748449e-261, 1.00000000e+000],\n",
-       "         [1.14035008e-227, 5.22505369e-295, 2.68012112e-204,\n",
-       "          2.35284962e-272, 1.00000000e+000],\n",
+       "         [5.85591377e-168, 0.00000000e+000, 1.00000000e+000,\n",
+       "          1.33402761e-217, 3.20776649e-270],\n",
+       "         [2.72704986e-060, 5.75124387e-301, 1.00000000e+000,\n",
+       "          1.26512087e-166, 1.69958582e-321],\n",
+       "         [5.60236043e-199, 1.47121093e-281, 1.00000000e+000,\n",
+       "          2.78041933e-196, 0.00000000e+000]],\n",
+       "\n",
+       "        [[1.00000000e+000, 2.36228819e-082, 5.55561625e-114,\n",
+       "          2.05865650e-176, 1.80598809e-234],\n",
+       "         [1.00000000e+000, 2.50043737e-059, 1.69710247e-076,\n",
+       "          1.04783052e-091, 2.33971462e-185],\n",
+       "         [1.00000000e+000, 5.62712722e-100, 7.48483274e-027,\n",
+       "          1.07765897e-125, 1.67149582e-245],\n",
        "...\n",
-       "         [1.84685777e-088, 1.00000000e+000, 4.89269192e-184,\n",
-       "          7.38762399e-064, 0.00000000e+000],\n",
-       "         [1.35107715e-133, 1.00000000e+000, 3.44031578e-169,\n",
-       "          1.46510116e-077, 0.00000000e+000],\n",
-       "         [4.53965711e-046, 1.00000000e+000, 3.19458160e-159,\n",
-       "          1.49103047e-050, 0.00000000e+000]],\n",
-       "\n",
-       "        [[8.52094383e-026, 4.79059477e-012, 4.53943617e-011,\n",
-       "          1.00000000e+000, 1.74643648e-078],\n",
-       "         [1.38847319e-011, 1.05170246e-001, 1.79553539e-003,\n",
-       "          8.93034219e-001, 4.16854810e-060],\n",
-       "         [1.94441605e-018, 2.32528695e-001, 6.03540293e-018,\n",
-       "          7.67471305e-001, 1.62594133e-063],\n",
+       "         [1.70067137e-147, 9.34377628e-001, 4.99501091e-219,\n",
+       "          4.00596693e-020, 6.56223724e-002],\n",
+       "         [4.68060942e-292, 2.79727331e-057, 7.67468697e-274,\n",
+       "          3.31483923e-113, 1.00000000e+000],\n",
+       "         [1.56625556e-290, 5.12228239e-203, 0.00000000e+000,\n",
+       "          7.96478893e-209, 1.00000000e+000]],\n",
+       "\n",
+       "        [[0.00000000e+000, 0.00000000e+000, 1.00000000e+000,\n",
+       "          7.41149506e-014, 3.43292168e-208],\n",
+       "         [0.00000000e+000, 0.00000000e+000, 1.00000000e+000,\n",
+       "          3.35277996e-020, 1.23606061e-182],\n",
+       "         [0.00000000e+000, 0.00000000e+000, 1.00000000e+000,\n",
+       "          1.34830152e-088, 6.75279657e-289],\n",
        "         ...,\n",
-       "         [9.17447962e-033, 2.93347875e-014, 2.02552628e-025,\n",
-       "          1.00000000e+000, 5.95661433e-087],\n",
-       "         [4.71956594e-039, 4.02493334e-007, 2.37477741e-022,\n",
-       "          9.99999598e-001, 7.76581052e-089],\n",
-       "         [1.47529013e-027, 2.33452643e-014, 3.10069646e-020,\n",
-       "          1.00000000e+000, 4.83126180e-091]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_ic</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.57 -0.8283 ... 1.097 0.2006</div><input id='attrs-047980d7-fa9a-4ee5-9d37-7a6dcf09fb4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-047980d7-fa9a-4ee5-9d37-7a6dcf09fb4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ca5e83b-27bd-4a1d-b7e3-22e55af93ba2' class='xr-var-data-in' type='checkbox'><label for='data-9ca5e83b-27bd-4a1d-b7e3-22e55af93ba2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-5.70004358e-01, -8.28263292e-01,  2.12865719e-01,\n",
-       "        -1.22589604e+00, -7.38455183e-01,  1.38562947e+00,\n",
-       "        -5.14147314e-01,  8.78378147e-01,  6.29759498e-01,\n",
-       "        -4.48075605e-01, -1.65703227e-01,  9.40024262e-01,\n",
-       "        -1.30760455e-02, -4.49690187e-01,  6.11037300e-01,\n",
-       "        -9.42668616e-01,  1.70679147e+00, -1.52535825e+00,\n",
-       "         1.57013610e+00,  1.83215761e-01,  2.38784640e-04,\n",
-       "        -4.95682078e-01, -4.66365955e-01,  2.51903440e-01,\n",
-       "         3.30163556e-01, -4.77494859e-03, -2.09518470e+00,\n",
-       "         6.23561933e-01, -1.23929521e+00, -2.32219278e-01,\n",
-       "        -4.25066724e-01,  1.18733217e-01,  3.72339384e-01,\n",
-       "        -1.30904663e-01, -1.40000993e-01, -1.58417383e+00,\n",
-       "        -4.68317692e-01, -7.11586976e-01,  2.43477244e-01,\n",
-       "         9.58822534e-01, -5.11833848e-03,  3.42326203e-01,\n",
-       "        -2.29935048e-01,  1.59822529e+00,  1.33733438e+00,\n",
-       "        -5.59368283e-01, -4.92588819e-01, -5.61527404e-01,\n",
-       "        -1.87151514e+00, -5.51304365e-01,  7.80311840e-01,\n",
-       "         1.46567444e+00,  1.52603877e-01,  2.36093837e+00,\n",
-       "        -6.71137980e-01, -4.61363625e-01, -1.32707128e+00,\n",
-       "        -7.97252508e-02, -4.19876746e-01, -4.82346713e-01,\n",
+       "         [0.00000000e+000, 0.00000000e+000, 1.00000000e+000,\n",
+       "          3.64560450e-136, 2.37344522e-254],\n",
+       "         [8.47239771e-245, 0.00000000e+000, 1.00000000e+000,\n",
+       "          3.10236927e-080, 6.69291948e-261],\n",
+       "         [0.00000000e+000, 0.00000000e+000, 1.00000000e+000,\n",
+       "          3.83361668e-147, 0.00000000e+000]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_oc</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.9053 0.7179 ... 0.2239 -2.288</div><input id='attrs-63118d5f-e56f-457d-b362-701e608883a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63118d5f-e56f-457d-b362-701e608883a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42bb141d-bede-4279-9586-d9c1156910a8' class='xr-var-data-in' type='checkbox'><label for='data-42bb141d-bede-4279-9586-d9c1156910a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-9.05341242e-01,  7.17884245e-01, -3.28879825e-01,\n",
+       "        -1.17032858e+00, -1.75984416e+00,  3.39189730e-01,\n",
+       "        -9.63133847e-02,  2.63168042e-01, -2.95942989e-01,\n",
+       "        -3.12743219e-01, -3.16287223e-01,  4.60160032e-01,\n",
+       "         1.53961144e+00,  6.40645628e-01,  1.26837566e+00,\n",
+       "        -1.49846126e+00, -2.17504290e-01, -3.74738055e-01,\n",
+       "         3.85529196e-01, -9.30123993e-01,  1.60102244e+00,\n",
+       "         1.46178582e-01, -1.74851523e+00, -2.73354870e-01,\n",
+       "        -1.26881028e-01, -2.04446511e+00,  5.83218291e-01,\n",
+       "         8.90517568e-01,  1.27943010e+00, -1.05210252e+00,\n",
+       "        -4.55170680e-01,  4.65248473e-01,  2.91412837e-01,\n",
+       "         1.82274068e-01,  1.50876876e+00,  1.02757312e+00,\n",
+       "         5.90787742e-01,  1.33942014e+00, -9.59685550e-01,\n",
+       "         8.67669031e-01, -6.64157083e-01, -4.11412227e-01,\n",
+       "         2.04724961e-01, -1.46535453e+00, -1.08331647e+00,\n",
+       "         1.28845111e+00, -6.80146524e-01, -6.21886900e-01,\n",
+       "        -2.49785007e-01,  4.99936979e-01,  6.81855657e-01,\n",
+       "        -8.96727753e-01,  1.13663387e+00,  2.86390811e-01,\n",
+       "         1.74638609e-01,  1.29787667e+00,  3.59992050e-01,\n",
+       "        -6.90060287e-01,  3.48021926e-01,  2.92849183e+00,\n",
        "...\n",
-       "        -1.61813217e+00,  3.23956615e-01, -2.58347090e-01,\n",
-       "         1.09891379e+00,  1.04963130e-02,  1.21951803e+00,\n",
-       "        -8.88469247e-01,  2.40129297e-01,  8.74456788e-02,\n",
-       "        -1.14112180e-01,  9.70872675e-01,  5.79073453e-02,\n",
-       "         1.95989732e-01,  1.34320229e+00,  8.83436743e-03,\n",
-       "         2.61360349e-01,  1.89287929e-01,  1.42289282e+00,\n",
-       "        -4.10941567e-01, -3.60285728e-02,  6.54785228e-01,\n",
-       "        -1.33499268e-02, -8.97305571e-02,  8.75881939e-01,\n",
-       "         1.49457334e+00, -3.00572733e-01, -9.10286901e-01,\n",
-       "         1.08817750e+00, -1.08538576e+00,  8.05863335e-01,\n",
-       "        -9.09648592e-01, -4.31780695e-01,  7.69342341e-01,\n",
-       "        -9.71707445e-01, -1.54695370e+00,  9.20746449e-01,\n",
-       "         1.12597739e+00,  1.21455611e+00,  8.84523243e-01,\n",
-       "        -1.86645209e+00,  6.68284401e-01, -1.05267736e-01,\n",
-       "         2.12913417e+00, -3.54292999e-01,  2.77048772e-02,\n",
-       "         2.17053506e+00, -9.55429129e-01, -7.62062828e-01,\n",
-       "         2.31270806e+00, -5.42923830e-01, -1.20304858e+00,\n",
-       "        -4.31171792e-01, -8.12882960e-01,  9.24038908e-01,\n",
-       "        -1.41725247e-01,  2.80235277e-01, -6.97680233e-01,\n",
-       "         1.09738313e+00,  2.00559485e-01]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e893448f-611c-4906-9fba-08e80030d7e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-e893448f-611c-4906-9fba-08e80030d7e2' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e80b8d4-d739-4fd5-9cb5-fcac73706f57' class='xr-index-data-in' type='checkbox'/><label for='index-1e80b8d4-d739-4fd5-9cb5-fcac73706f57' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5982c216-3e89-4f07-8f1c-8f41205f67fc' class='xr-index-data-in' type='checkbox'/><label for='index-5982c216-3e89-4f07-8f1c-8f41205f67fc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2cf836c7-618a-4928-a039-09f97a362d27' class='xr-index-data-in' type='checkbox'/><label for='index-2cf836c7-618a-4928-a039-09f97a362d27' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-43fae5e1-d3d1-44ce-ab03-d0e73161f375' class='xr-index-data-in' type='checkbox'/><label for='index-43fae5e1-d3d1-44ce-ab03-d0e73161f375' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf6df1fc-008c-4983-ba20-a6e7098da174' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf6df1fc-008c-4983-ba20-a6e7098da174' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:07.842667</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.3.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "        -5.65506449e-03, -6.88559269e-01, -1.15336900e+00,\n",
+       "         4.50242476e-02, -1.22023018e+00, -1.12923423e-01,\n",
+       "         8.96462680e-01, -4.53947417e-01, -1.43414180e+00,\n",
+       "         5.12639956e-01, -5.77965096e-01,  1.45547667e+00,\n",
+       "         9.42311310e-01, -2.51584255e-02,  1.34739792e+00,\n",
+       "         9.75619376e-01, -8.27786727e-01,  4.29218916e-01,\n",
+       "         5.48677761e-02,  6.13713786e-01,  6.32482450e-01,\n",
+       "         6.32275175e-01, -3.49138891e-01,  1.14880756e+00,\n",
+       "         1.87190434e+00,  2.65111975e-01,  3.48956524e-01,\n",
+       "         1.44658189e+00,  4.44854495e-01, -1.37543695e+00,\n",
+       "         8.96143831e-01,  7.94642210e-01, -1.47775605e+00,\n",
+       "         4.12050582e-01,  3.34975467e-01, -1.52223671e+00,\n",
+       "         4.88368294e-01, -4.60597001e-01,  7.21544987e-01,\n",
+       "         7.24496350e-01,  1.08738153e+00,  2.10536855e+00,\n",
+       "         1.06044930e-01,  4.42705960e-01, -1.24437928e+00,\n",
+       "         9.33764916e-01,  1.03229110e+00,  3.41510005e-01,\n",
+       "         1.66154038e+00,  6.77411994e-01,  1.15605184e-01,\n",
+       "        -3.40847679e-01, -1.24381891e+00,  1.11772996e+00,\n",
+       "        -6.64756393e-01,  8.18899876e-01,  5.57772193e-01,\n",
+       "         2.23888427e-01, -2.28764840e+00]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90d1ccbc-d111-430f-a374-770de4cedb87' class='xr-section-summary-in' type='checkbox'  ><label for='section-90d1ccbc-d111-430f-a374-770de4cedb87' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b59a59fe-ee98-4416-bd97-6b553febe491' class='xr-index-data-in' type='checkbox'/><label for='index-b59a59fe-ee98-4416-bd97-6b553febe491' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf8068af-a071-4d4f-b90e-70e070077fd3' class='xr-index-data-in' type='checkbox'/><label for='index-cf8068af-a071-4d4f-b90e-70e070077fd3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb795b4d-e912-4bd3-9987-a26335be7e3c' class='xr-index-data-in' type='checkbox'/><label for='index-fb795b4d-e912-4bd3-9987-a26335be7e3c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db6ac976-6965-4048-888e-c870484e76e5' class='xr-index-data-in' type='checkbox'/><label for='index-db6ac976-6965-4048-888e-c870484e76e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8654715b-739a-41d9-a17a-f4466f8f9eed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8654715b-739a-41d9-a17a-f4466f8f9eed' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:15:43.538814</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.9.2</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_prior_predictive3891d253-9b5a-4933-92b5-c05b714d083b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_prior_predictive3891d253-9b5a-4933-92b5-c05b714d083b\" class = \"xr-section-summary\">prior_predictive</label>\n",
+       "                  <input id=\"idata_prior_predictivebd0c7b3c-8657-45ce-8f3c-1638b1361f65\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_prior_predictivebd0c7b3c-8657-45ce-8f3c-1638b1361f65\" class = \"xr-section-summary\">prior_predictive</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -3213,31 +3254,31 @@
        "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
        "  * obs      (obs) int64 0 1 2 3 4 5 6 7 8 ... 892 893 894 895 896 897 898 899\n",
        "Data variables:\n",
-       "    y_cat    (chain, draw, obs) int64 4 4 4 4 4 4 4 4 4 4 ... 3 1 1 3 3 3 3 3 3\n",
+       "    y_cat    (chain, draw, obs) int64 2 2 2 3 2 2 2 2 2 2 ... 2 3 2 2 2 2 2 2 2\n",
        "Attributes:\n",
-       "    created_at:                 2023-07-13T17:29:07.844409\n",
-       "    arviz_version:              0.15.1\n",
+       "    created_at:                 2023-12-08T23:15:43.547264\n",
+       "    arviz_version:              0.16.1\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1fe7d277-4ae0-4238-a897-5b7b2cb240ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1fe7d277-4ae0-4238-a897-5b7b2cb240ad' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-05b93e87-e538-498e-bbd0-e9581710b267' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05b93e87-e538-498e-bbd0-e9581710b267' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-42219475-11ff-45d6-9c58-bf5ea8e76147' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42219475-11ff-45d6-9c58-bf5ea8e76147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fa47161-83c1-40a3-8ba1-47fa3529a9d9' class='xr-var-data-in' type='checkbox'><label for='data-1fa47161-83c1-40a3-8ba1-47fa3529a9d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-292b4843-e362-424b-969f-df33e2615b44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-292b4843-e362-424b-969f-df33e2615b44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6851bcb-45fa-4a70-8fae-7684b4939e66' class='xr-var-data-in' type='checkbox'><label for='data-f6851bcb-45fa-4a70-8fae-7684b4939e66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-f3c348b2-f759-40fa-9de2-5d976904eb33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3c348b2-f759-40fa-9de2-5d976904eb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d585f01-dc36-4144-9047-c1e76d7827b8' class='xr-var-data-in' type='checkbox'><label for='data-3d585f01-dc36-4144-9047-c1e76d7827b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-923690a1-8d01-4498-8828-85b0351eec75' class='xr-section-summary-in' type='checkbox'  checked><label for='section-923690a1-8d01-4498-8828-85b0351eec75' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4 4 4 4 4 4 4 4 ... 1 1 3 3 3 3 3 3</div><input id='attrs-54c339f8-585d-41eb-a545-f2a3dcd32067' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54c339f8-585d-41eb-a545-f2a3dcd32067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37e0ffdb-a9fc-40f3-a823-eef8221c38d2' class='xr-var-data-in' type='checkbox'><label for='data-37e0ffdb-a9fc-40f3-a823-eef8221c38d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[4, 4, 4, ..., 4, 4, 4],\n",
-       "        [4, 4, 4, ..., 4, 4, 4],\n",
-       "        [0, 0, 1, ..., 1, 0, 0],\n",
+       "    inference_library_version:  5.9.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-54ccca3f-d218-43e6-8cec-3e0e8b96d758' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54ccca3f-d218-43e6-8cec-3e0e8b96d758' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-32e4de9e-f7bc-4946-b464-6cd2e9fe259b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32e4de9e-f7bc-4946-b464-6cd2e9fe259b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-32faae88-6e36-48ca-bd75-458779caefff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32faae88-6e36-48ca-bd75-458779caefff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51fe1f44-a342-471e-bcf8-ec05aca23db9' class='xr-var-data-in' type='checkbox'><label for='data-51fe1f44-a342-471e-bcf8-ec05aca23db9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-ddbcda6b-c2fc-4109-bb2f-b70383674666' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddbcda6b-c2fc-4109-bb2f-b70383674666' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ba4f9f7-6909-4e9a-9d46-3cc77e4067ba' class='xr-var-data-in' type='checkbox'><label for='data-7ba4f9f7-6909-4e9a-9d46-3cc77e4067ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-c577fb56-3774-4de6-b88a-d6e624ac58c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c577fb56-3774-4de6-b88a-d6e624ac58c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be1f50e8-f510-4d6e-ad2c-1fb88a7df756' class='xr-var-data-in' type='checkbox'><label for='data-be1f50e8-f510-4d6e-ad2c-1fb88a7df756' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16940d89-b366-4803-9bc1-0c5e5ebffda1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16940d89-b366-4803-9bc1-0c5e5ebffda1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(chain, draw, obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 3 2 2 2 2 ... 3 2 2 2 2 2 2 2</div><input id='attrs-b6007316-823b-45d4-9d4f-73efa5aa44f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6007316-823b-45d4-9d4f-73efa5aa44f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf6c716b-a7c5-4e14-bef7-460954527e4b' class='xr-var-data-in' type='checkbox'><label for='data-cf6c716b-a7c5-4e14-bef7-460954527e4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[2, 2, 2, ..., 2, 2, 2],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [4, 4, 4, ..., 3, 4, 4],\n",
        "        ...,\n",
-       "        [4, 4, 4, ..., 4, 4, 4],\n",
-       "        [1, 1, 1, ..., 1, 1, 1],\n",
-       "        [3, 3, 3, ..., 3, 3, 3]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6e5ef8f-ecd2-45fc-aa88-51fdabe1d21e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e6e5ef8f-ecd2-45fc-aa88-51fdabe1d21e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-307256ef-4eda-4e00-8b8d-c0821e7b292e' class='xr-index-data-in' type='checkbox'/><label for='index-307256ef-4eda-4e00-8b8d-c0821e7b292e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-94b91174-075f-45a0-89b0-e887aa532340' class='xr-index-data-in' type='checkbox'/><label for='index-94b91174-075f-45a0-89b0-e887aa532340' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd5b96c2-415f-4850-95f5-bc3f8b7c0d98' class='xr-index-data-in' type='checkbox'/><label for='index-bd5b96c2-415f-4850-95f5-bc3f8b7c0d98' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7fe1fd0c-1326-43d3-8a9f-3c0c6c3592c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fe1fd0c-1326-43d3-8a9f-3c0c6c3592c8' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:07.844409</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.3.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "        [1, 1, 4, ..., 1, 1, 4],\n",
+       "        [4, 4, 4, ..., 1, 4, 4],\n",
+       "        [2, 2, 2, ..., 2, 2, 2]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b127692-a3d7-47d7-aeeb-2293bd833da0' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b127692-a3d7-47d7-aeeb-2293bd833da0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-59695d71-2d1e-42e8-9f0f-5251ba2fa9a6' class='xr-index-data-in' type='checkbox'/><label for='index-59695d71-2d1e-42e8-9f0f-5251ba2fa9a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9142bb75-0ae6-403c-baa2-70e8c720af32' class='xr-index-data-in' type='checkbox'/><label for='index-9142bb75-0ae6-403c-baa2-70e8c720af32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f4636112-581a-4c24-ba29-9aee5cf8d2a4' class='xr-index-data-in' type='checkbox'/><label for='index-f4636112-581a-4c24-ba29-9aee5cf8d2a4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-367eda29-9df6-4cdf-a96e-44fba5a255cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-367eda29-9df6-4cdf-a96e-44fba5a255cd' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:15:43.547264</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.9.2</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_observed_data9566152e-a2f7-4322-99e8-9850618bfed5\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_observed_data9566152e-a2f7-4322-99e8-9850618bfed5\" class = \"xr-section-summary\">observed_data</label>\n",
+       "                  <input id=\"idata_observed_data51eb025c-ab87-46d7-8668-aabb85911d33\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_data51eb025c-ab87-46d7-8668-aabb85911d33\" class = \"xr-section-summary\">observed_data</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -3611,10 +3652,10 @@
        "Data variables:\n",
        "    y_cat    (obs) int64 2 2 2 1 1 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 1 2 2 2 2 2\n",
        "Attributes:\n",
-       "    created_at:                 2023-07-13T17:29:07.844758\n",
-       "    arviz_version:              0.15.1\n",
+       "    created_at:                 2023-12-08T23:15:43.551429\n",
+       "    arviz_version:              0.16.1\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.3.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e0244726-b6c8-4d07-9ac6-2a1847b25601' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e0244726-b6c8-4d07-9ac6-2a1847b25601' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-be8fbe44-7ff2-4ad1-b60e-f880380d8f14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be8fbe44-7ff2-4ad1-b60e-f880380d8f14' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-9244ffde-07c3-4a47-a7fb-c2bb37a9d898' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9244ffde-07c3-4a47-a7fb-c2bb37a9d898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7518548b-ccac-4393-8404-a67d7dbcb70f' class='xr-var-data-in' type='checkbox'><label for='data-7518548b-ccac-4393-8404-a67d7dbcb70f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba29d64c-351d-4451-a3ea-3d50b0a388f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba29d64c-351d-4451-a3ea-3d50b0a388f8' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 1 1 2 2 2 ... 2 2 1 2 2 2 2 2</div><input id='attrs-5a0dbebd-eea4-4188-8aa7-e70166a0dd6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a0dbebd-eea4-4188-8aa7-e70166a0dd6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53aca934-2d04-475e-a4fe-61711b2cc7be' class='xr-var-data-in' type='checkbox'><label for='data-53aca934-2d04-475e-a4fe-61711b2cc7be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 4, 4, 1, 2, 0, 3,\n",
+       "    inference_library_version:  5.9.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af563bbf-2b63-4254-9f1e-cc7899a01d0c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af563bbf-2b63-4254-9f1e-cc7899a01d0c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>obs</span>: 900</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-21726e73-8f6e-479b-a923-8f6c05d735f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21726e73-8f6e-479b-a923-8f6c05d735f0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 895 896 897 898 899</div><input id='attrs-a8412510-88df-45bc-8e69-c85536002fee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8412510-88df-45bc-8e69-c85536002fee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dcdca04-85b5-447d-9106-3d22869a115e' class='xr-var-data-in' type='checkbox'><label for='data-0dcdca04-85b5-447d-9106-3d22869a115e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 897, 898, 899])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74420bbc-bb36-491e-a322-055c3d43b14c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74420bbc-bb36-491e-a322-055c3d43b14c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y_cat</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 1 1 2 2 2 ... 2 2 1 2 2 2 2 2</div><input id='attrs-d1976e2c-1fec-4b39-b090-7999828f2fd6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1976e2c-1fec-4b39-b090-7999828f2fd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74146c73-e62c-4448-90e0-8036047e3bed' class='xr-var-data-in' type='checkbox'><label for='data-74146c73-e62c-4448-90e0-8036047e3bed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 4, 4, 1, 2, 0, 3,\n",
        "       2, 4, 2, 3, 2, 0, 0, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 1, 2, 3, 2, 2,\n",
        "       2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2,\n",
        "       2, 2, 3, 2, 3, 2, 1, 4, 1, 2, 2, 2, 1, 3, 2, 2, 2, 4, 3, 2, 2, 2,\n",
@@ -3654,10 +3695,10 @@
        "       0, 2, 2, 3, 2, 2, 1, 2, 3, 2, 2, 2, 2, 4, 1, 2, 2, 2, 2, 2, 3, 2,\n",
        "       1, 2, 1, 0, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 0, 2, 1, 2, 2, 2, 2, 2,\n",
        "       1, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 1, 3, 2, 2, 4, 4, 2, 2, 2,\n",
-       "       1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72cd0842-a9e1-4915-941e-7bc73e35ef0e' class='xr-section-summary-in' type='checkbox'  ><label for='section-72cd0842-a9e1-4915-941e-7bc73e35ef0e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b649d3d3-2ef9-4169-ba46-2c41001ff133' class='xr-index-data-in' type='checkbox'/><label for='index-b649d3d3-2ef9-4169-ba46-2c41001ff133' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "            ...\n",
-       "            890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eee7b226-1aab-4a21-8ede-0156f6675133' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eee7b226-1aab-4a21-8ede-0156f6675133' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-13T17:29:07.844758</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.3.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "       1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56931f76-8488-4d7c-b997-b992038a6e5f' class='xr-section-summary-in' type='checkbox'  ><label for='section-56931f76-8488-4d7c-b997-b992038a6e5f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f0501aa-dbe4-4bb4-96aa-5a1cbf227d34' class='xr-index-data-in' type='checkbox'/><label for='index-1f0501aa-dbe4-4bb4-96aa-5a1cbf227d34' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;obs&#x27;, length=900))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e00b34ed-4eac-4ff5-bc1b-d132ddac1b51' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e00b34ed-4eac-4ff5-bc1b-d132ddac1b51' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-12-08T23:15:43.551429</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.9.2</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
@@ -4065,26 +4106,26 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>beta_ic</th>\n",
-       "      <td>0.002</td>\n",
+       "      <td>-0.006</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.002</td>\n",
+       "      <td>-0.007</td>\n",
+       "      <td>-0.006</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1870.0</td>\n",
-       "      <td>1999.0</td>\n",
+       "      <td>3471.0</td>\n",
+       "      <td>2790.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_oc</th>\n",
-       "      <td>-0.004</td>\n",
+       "      <td>-0.005</td>\n",
        "      <td>0.0</td>\n",
        "      <td>-0.005</td>\n",
        "      <td>-0.004</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>881.0</td>\n",
-       "      <td>1105.0</td>\n",
+       "      <td>3971.0</td>\n",
+       "      <td>2946.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -4093,8 +4134,8 @@
       ],
       "text/plain": [
        "          mean   sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "beta_ic  0.002  0.0   0.002    0.002        0.0      0.0    1870.0    1999.0   \n",
-       "beta_oc -0.004  0.0  -0.005   -0.004        0.0      0.0     881.0    1105.0   \n",
+       "beta_ic -0.006  0.0  -0.007   -0.006        0.0      0.0    3471.0    2790.0   \n",
+       "beta_oc -0.005  0.0  -0.005   -0.004        0.0      0.0    3971.0    2946.0   \n",
        "\n",
        "         r_hat  \n",
        "beta_ic    1.0  \n",
@@ -4137,7 +4178,7 @@
     {
      "data": {
       "text/plain": [
-       "-2.215565260325244"
+       "0.7380233924649465"
       ]
      },
      "execution_count": 8,
@@ -4166,7 +4207,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x1000 with 1 Axes>"
       ]
@@ -4205,517 +4246,196 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "alts_probs\n",
+       "ec    0.104173\n",
+       "er    0.051373\n",
+       "gc    0.517023\n",
+       "gr    0.240261\n",
+       "hp    0.087170\n",
+       "Name: p, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idata_m1[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"]).to_dataframe()[\"p\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 1011,
+       "width": 2011
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n",
+    "ax = axs[0]\n",
+    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
+    "predicted_shares = idata_m1[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])\n",
+    "ci_lb = idata_m1[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
+    "ci_ub = idata_m1[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
+    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
+    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
+    "ax.scatter(\n",
+    "    counts / counts.sum(),\n",
+    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
+    "    label=\"Observed Shares\",\n",
+    "    color=\"red\",\n",
+    "    s=100,\n",
+    ")\n",
+    "ax.hlines(\n",
+    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], ci_lb, ci_ub, label=\"Predicted 95% Interval\", color=\"black\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Observed V Predicted Shares\")\n",
+    "az.plot_ppc(idata_m1, ax=axs[1])\n",
+    "axs[1].set_title(\"Posterior Predictive Checks\")\n",
+    "ax.set_xlabel(\"Shares\")\n",
+    "ax.set_ylabel(\"Heating System\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see here that the model is fairly inadequate, and fails quite dramatically to recapture the posterior predictive distribution. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Improved Model: Adding Alternative Specific Intercepts\n",
+    "\n",
+    "We can address some of the issues with the prior model specification by adding intercept terms for each of the unique alternatives `gr, gc, ec, er`. These terms will absorb some of the error seen in the last model by allowing us to control some of the heterogenity of utility measures across products. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_ic, beta_oc, y_cat]\n",
+      "Compiling...\n",
+      "Compilation time = 0:00:02.168994\n",
+      "Sampling...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d00909ee14a443bad05112fa666925e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d0cac0cfe9d402f9a01014ec53889bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3d186a6b7d6419497818792aa88c487",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fda7e089bfc4cbaa3dab5984764b444",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling time = 0:00:15.558693\n",
+      "Transforming variables...\n",
+      "Transformation time = 0:00:00.521186\n",
+      "Computing Log Likelihood...\n",
+      "Log Likelihood time = 0:00:00.672748\n",
+      "Sampling: [y_cat]\n"
+     ]
+    },
     {
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-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;p&#x27; (alts_probs: 5)&gt;\n",
-       "array([0.08414602, 0.13748865, 0.26918857, 0.38213088, 0.12704589])\n",
-       "Coordinates:\n",
-       "  * alts_probs  (alts_probs) &lt;U2 &#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'p'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>alts_probs</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-20000efd-6bb8-4485-a7a1-db3cbb12131d' class='xr-array-in' type='checkbox' checked><label for='section-20000efd-6bb8-4485-a7a1-db3cbb12131d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.08415 0.1375 0.2692 0.3821 0.127</span></div><div class='xr-array-data'><pre>array([0.08414602, 0.13748865, 0.26918857, 0.38213088, 0.12704589])</pre></div></div></li><li class='xr-section-item'><input id='section-1df3266c-7169-447b-a0b3-ce173f3392f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1df3266c-7169-447b-a0b3-ce173f3392f4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>alts_probs</span></div><div class='xr-var-dims'>(alts_probs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;ec&#x27; &#x27;er&#x27; &#x27;gc&#x27; &#x27;gr&#x27; &#x27;hp&#x27;</div><input id='attrs-d77cc202-fe23-46a4-a26a-f72162e41be1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d77cc202-fe23-46a4-a26a-f72162e41be1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f688d98-24d4-400d-9cbe-b4d539bc96ae' class='xr-var-data-in' type='checkbox'><label for='data-0f688d98-24d4-400d-9cbe-b4d539bc96ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a23d3fc9-eb6e-48b9-8d2d-be2bba30798b' class='xr-section-summary-in' type='checkbox'  ><label for='section-a23d3fc9-eb6e-48b9-8d2d-be2bba30798b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>alts_probs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3774f4bf-ef45-41e7-864c-64b9454dd4f7' class='xr-index-data-in' type='checkbox'/><label for='index-3774f4bf-ef45-41e7-864c-64b9454dd4f7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;ec&#x27;, &#x27;er&#x27;, &#x27;gc&#x27;, &#x27;gr&#x27;, &#x27;hp&#x27;], dtype=&#x27;object&#x27;, name=&#x27;alts_probs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0d8347e-1a96-4f11-a055-7c456f69db8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0d8347e-1a96-4f11-a055-7c456f69db8c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
       ],
       "text/plain": [
-       "<xarray.DataArray 'p' (alts_probs: 5)>\n",
-       "array([0.08414602, 0.13748865, 0.26918857, 0.38213088, 0.12704589])\n",
-       "Coordinates:\n",
-       "  * alts_probs  (alts_probs) <U2 'ec' 'er' 'gc' 'gr' 'hp'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idata_m1[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
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-      "text/plain": [
-       "<Figure size 2000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1011,
-       "width": 2011
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(1, 2, figsize=(20, 10))\n",
-    "ax = axs[0]\n",
-    "counts = wide_heating_df.groupby(\"depvar\")[\"idcase\"].count()\n",
-    "predicted_shares = idata_m1[\"posterior\"][\"p\"].mean(dim=[\"chain\", \"draw\", \"obs\"])\n",
-    "ci_lb = idata_m1[\"posterior\"][\"p\"].quantile(0.025, dim=[\"chain\", \"draw\", \"obs\"])\n",
-    "ci_ub = idata_m1[\"posterior\"][\"p\"].quantile(0.975, dim=[\"chain\", \"draw\", \"obs\"])\n",
-    "ax.scatter(ci_lb, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
-    "ax.scatter(ci_ub, [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], color=\"k\", s=2)\n",
-    "ax.scatter(\n",
-    "    counts / counts.sum(),\n",
-    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"],\n",
-    "    label=\"Observed Shares\",\n",
-    "    color=\"red\",\n",
-    "    s=100,\n",
-    ")\n",
-    "ax.hlines(\n",
-    "    [\"ec\", \"er\", \"gc\", \"gr\", \"hp\"], ci_lb, ci_ub, label=\"Predicted 95% Interval\", color=\"black\"\n",
-    ")\n",
-    "ax.legend()\n",
-    "ax.set_title(\"Observed V Predicted Shares\")\n",
-    "az.plot_ppc(idata_m1, ax=axs[1])\n",
-    "axs[1].set_title(\"Posterior Predictive Checks\")\n",
-    "ax.set_xlabel(\"Shares\")\n",
-    "ax.set_ylabel(\"Heating System\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see here that the model is fairly inadequate, and fails quite dramatically to recapture the posterior predictive distribution. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Improved Model: Adding Alternative Specific Intercepts\n",
-    "\n",
-    "We can address some of the issues with the prior model specification by adding intercept terms for each of the unique alternatives `gr, gc, ec, er`. These terms will absorb some of the error seen in the last model by allowing us to control some of the heterogenity of utility measures across products. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: [alpha, beta_ic, beta_oc, y_cat]\n",
-      "/Users/nathanielforde/mambaforge/envs/pymc_examples_new/lib/python3.9/site-packages/pymc/sampling/mcmc.py:243: UserWarning: Use of external NUTS sampler is still experimental\n",
-      "  warnings.warn(\"Use of external NUTS sampler is still experimental\", UserWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Compiling...\n",
-      "Compilation time =  0:00:01.339706\n",
-      "Sampling...\n"
-     ]
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-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+       "<IPython.core.display.HTML object>"
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@@ -4723,125 +4443,118 @@
     },
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-       "version_minor": 0
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+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='4000' class='' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [4000/4000 00:10&lt;00:00]\n",
+       "    </div>\n",
+       "    "
+      ],
       "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+       "<IPython.core.display.HTML object>"
       ]
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      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sampling time =  0:00:15.309789\n",
-      "Transforming variables...\n",
-      "Transformation time =  0:00:00.289041\n",
-      "Computing Log Likelihood...\n"
-     ]
-    },
     {
      "data": {
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-       "<!-- Generated by graphviz version 7.1.0 (0)\n",
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        "<!-- Pages: 1 -->\n",
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        "<title>clusteralts_intercepts (4)</title>\n",
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        "</g>\n",
-       "<!-- beta_oc -->\n",
+       "<!-- beta_ic -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>beta_oc</title>\n",
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-       "<text text-anchor=\"middle\" x=\"41.72\" y=\"-279.73\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<title>beta_ic</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"48.97\" cy=\"-326.25\" rx=\"48.97\" ry=\"42.25\"/>\n",
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+       "<text text-anchor=\"middle\" x=\"48.97\" y=\"-304.32\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "</g>\n",
-       "<!-- beta_oc&#45;&gt;p -->\n",
+       "<!-- beta_ic&#45;&gt;p -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>beta_oc&#45;&gt;p</title>\n",
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+       "<title>beta_ic&#45;&gt;p</title>\n",
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-       "<!-- beta_ic -->\n",
+       "<!-- beta_oc -->\n",
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+       "<title>beta_oc</title>\n",
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        "</g>\n",
-       "<!-- beta_ic&#45;&gt;p -->\n",
+       "<!-- beta_oc&#45;&gt;p -->\n",
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       "text/plain": [
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      },
      "execution_count": 12,
@@ -4868,7 +4581,9 @@
     "    u1 = alphas[1] + beta_ic * wide_heating_df[\"ic.er\"] + beta_oc * wide_heating_df[\"oc.er\"]\n",
     "    u2 = alphas[2] + beta_ic * wide_heating_df[\"ic.gc\"] + beta_oc * wide_heating_df[\"oc.gc\"]\n",
     "    u3 = alphas[3] + beta_ic * wide_heating_df[\"ic.gr\"] + beta_oc * wide_heating_df[\"oc.gr\"]\n",
-    "    u4 = np.zeros(N)  # Outside Good\n",
+    "    u4 = (\n",
+    "        0 + beta_ic * wide_heating_df[\"ic.hp\"] + beta_oc * wide_heating_df[\"oc.hp\"]\n",
+    "    )  # Make one constant zero\n",
     "    s = pm.math.stack([u0, u1, u2, u3, u4]).T\n",
     "\n",
     "    ## Apply Softmax Transform\n",
@@ -4927,75 +4642,75 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>beta_ic</th>\n",
+       "      <td>-0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-0.000</td>\n",
-       "      <td>0.001</td>\n",
+       "      <td>-0.003</td>\n",
+       "      <td>-0.001</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>1215.0</td>\n",
-       "      <td>1612.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>2607.0</td>\n",
+       "      <td>2813.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta_oc</th>\n",
-       "      <td>-0.003</td>\n",
+       "      <td>-0.006</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>-0.005</td>\n",
-       "      <td>-0.001</td>\n",
+       "      <td>-0.008</td>\n",
+       "      <td>-0.003</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>1379.0</td>\n",
-       "      <td>1743.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>1259.0</td>\n",
+       "      <td>1520.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha[ec]</th>\n",
-       "      <td>1.039</td>\n",
-       "      <td>0.497</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>1.936</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>908.0</td>\n",
-       "      <td>1054.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>1.179</td>\n",
+       "      <td>0.379</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>1.900</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>1291.0</td>\n",
+       "      <td>1583.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha[er]</th>\n",
-       "      <td>1.077</td>\n",
-       "      <td>0.474</td>\n",
-       "      <td>0.216</td>\n",
-       "      <td>1.988</td>\n",
-       "      <td>0.016</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>839.0</td>\n",
-       "      <td>991.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>1.481</td>\n",
+       "      <td>0.310</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>2.080</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>1271.0</td>\n",
+       "      <td>1607.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha[gc]</th>\n",
-       "      <td>2.376</td>\n",
-       "      <td>0.309</td>\n",
-       "      <td>1.789</td>\n",
-       "      <td>2.953</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>814.0</td>\n",
-       "      <td>836.0</td>\n",
-       "      <td>1.01</td>\n",
+       "      <td>1.602</td>\n",
+       "      <td>0.211</td>\n",
+       "      <td>1.184</td>\n",
+       "      <td>1.989</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>1402.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha[gr]</th>\n",
-       "      <td>0.733</td>\n",
-       "      <td>0.373</td>\n",
-       "      <td>-0.031</td>\n",
-       "      <td>1.374</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>854.0</td>\n",
-       "      <td>947.0</td>\n",
-       "      <td>1.01</td>\n",
+       "      <td>0.266</td>\n",
+       "      <td>0.191</td>\n",
+       "      <td>-0.085</td>\n",
+       "      <td>0.634</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1379.0</td>\n",
+       "      <td>1507.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -5003,20 +4718,20 @@
       ],
       "text/plain": [
        "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
-       "beta_ic    0.001  0.000  -0.000    0.001      0.000    0.000    1215.0   \n",
-       "beta_oc   -0.003  0.001  -0.005   -0.001      0.000    0.000    1379.0   \n",
-       "alpha[ec]  1.039  0.497   0.076    1.936      0.016    0.012     908.0   \n",
-       "alpha[er]  1.077  0.474   0.216    1.988      0.016    0.012     839.0   \n",
-       "alpha[gc]  2.376  0.309   1.789    2.953      0.011    0.008     814.0   \n",
-       "alpha[gr]  0.733  0.373  -0.031    1.374      0.013    0.009     854.0   \n",
+       "beta_ic   -0.002  0.001  -0.003   -0.001      0.000    0.000    2607.0   \n",
+       "beta_oc   -0.006  0.001  -0.008   -0.003      0.000    0.000    1259.0   \n",
+       "alpha[ec]  1.179  0.379   0.491    1.900      0.011    0.008    1291.0   \n",
+       "alpha[er]  1.481  0.310   0.913    2.080      0.009    0.006    1271.0   \n",
+       "alpha[gc]  1.602  0.211   1.184    1.989      0.005    0.004    1467.0   \n",
+       "alpha[gr]  0.266  0.191  -0.085    0.634      0.005    0.004    1379.0   \n",
        "\n",
        "           ess_tail  r_hat  \n",
-       "beta_ic      1612.0   1.00  \n",
-       "beta_oc      1743.0   1.00  \n",
-       "alpha[ec]    1054.0   1.00  \n",
-       "alpha[er]     991.0   1.00  \n",
-       "alpha[gc]     836.0   1.01  \n",
-       "alpha[gr]     947.0   1.01  "
+       "beta_ic      2813.0    1.0  \n",
+       "beta_oc      1520.0    1.0  \n",
+       "alpha[ec]    1583.0    1.0  \n",
+       "alpha[er]    1607.0    1.0  \n",
+       "alpha[gc]    1402.0    1.0  \n",
+       "alpha[gr]    1507.0    1.0  "
       ]
      },
      "execution_count": 13,
@@ -5037,12 +4752,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
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FChX0zjvv6IUXXpCHh4cSEhJ08eJFLVu2TNOnTzcETqZOnapy5cqpRYsWKdb9888/69atW6ZtR0dHtW3bVm3atFH58uUNya1RUVE6d+6cjh07pn///dfqWYWnTZum3377zVBWuHBhdenSRS+88IJKlChhCh5cvXpVGzZs0NSpU03J/OHh4RowYICWLl2qfPnyWdWmJB0+fFhLliyR9CBQ8frrr6tly5YqU6aMHB0dFRcXp/Pnz2vv3r2mY9q3b6/Ro0ebthcvXmw2M3Za/v77b8N2u3btLA5aSPTtt9+aHVO6dGl16dJFtWvXVuHChU3l586d0+rVqzVjxgzdv39f0oMBJ++//77+/vtvi8nISY0cOVK7du0ylBUtWlS9e/dWgwYNTAH2gIAArVmzRr/99psCAwN15MgR/fLLL2m+98zStm1bjRkzxjTAJSYmxpR8bI3t27ebBdaTJ8w/SiwNAipQoECax82YMcOU5Jw7d2698847atSokQoVKiTpwT178OBB07WS6PLly3rnnXcMM0+7ubmpbdu2atmypSpUqCB3d3dJDxKWd+3apd9//127d+827T9v3jyVKVNGr776aqp9vHLlij788EPD96yjo6M6duyojh07qmzZsnJyclJMTIz27dunWbNmaf369UpISNCwYcP0zDPPpPk5ZNSOHTv00Ucfmc1cXrNmTXXs2FG1atVS7ty5TeW3b982fQ+uW7fO4koR3bp1M333zpgxwzDwplu3bipWrFiK/alcubLF8gMHDqh///6GALSXl5c6duyopk2bqmzZsqbBGMHBwdq2bZumTJmiEydOmPb/6aefVLp0adWrVy/F9hPb+uqrrwxlLi4u6tatm9q2bSs/Pz85ODgoMjJSO3bs0PTp07V7925FR0ebBfHt5cUXX9SUKVMMZatWrdLRo0f1xhtvqFmzZobzZg/btm0z3b8+Pj7q3r27mjRpYvpdi4uL04EDB/Trr79q69atpuOCgoL0/fff6/vvv7eqHScnJ1WqVEkNGjRQxYoVVapUKfn6+ppej46O1smTJ7Vhwwb99ddfpns+ISFBn332mSpWrJjqNWfJ9evX9eWXX5rab9++vdq2batKlSrJxcVF8fHxunr1qjZu3Gg4LiEhQYMGDTL7G6FatWp69dVX5e/vbxpwEB8frxMnTmjp0qWaO3euaWKCS5cuaeDAgZo5c2aKSfQrVqwwfKbSg0Esr7/+uqpXr2747oiLi9Ply5d16tQpbd26VZs3b7Y4qQgAAAAAAAAAPI2Sx6lcXFzMnq+Hh4frvffe040bNwzlVapU0VtvvaW6deuaYtdXr17VypUrNXnyZMNqw4sWLVKRIkXUq1evFPtij2e/xYoV0+effy7pwaTyM2bMMLzWrVu3FPvj6elpsTyrnoVbkt74YHqcOnVKgwYNUnR0tKG8devW6ty5sypXrixnZ2fFxcXp2LFjWrhwoRYsWGCK8cbFxemzzz5T8eLFVb16dZvaHjFihCneW65cOfXo0UPPP/+8KZ59//59bdmyxfS+H7YjR46YJZHmz58/zePWrFljOp9eXl7q3r27mjVrZoo1xcTE6MSJE7pw4YLhuODgYPXo0cOwsraTk5OaNWumtm3bqmrVqqaxA1FRUdq/f79mzZplSPzcvHmzxowZo0GDBqXax5CQEPXp08dsVeNmzZrp9ddfV5UqVeTi4mK6DubNm6dFixYpISFBP/30k0qVKpXm55BRZ86cUe/evc2ScsuWLavXXntNderUUYECBUzjUoKDg3X8+HFt27ZNa9asMawMnSgxLiY9SGRO+l3Ztm3bFGO6klS+fPkMvZ8nfdyKrY4fP25WltHPOCNOnz6tlStXKiEhQe7u7urSpYtatGih5557To6OjkpISNCxY8f0+++/a8WKFabjoqOj9dlnn2nWrFlptpGV97gkff7554a+Sg/GkHTq1ElNmjRRpUqVTItJJI61OnTokNavX69t27alWb8tJkyYoHHjxhnKOnbsqM8//5zYMQAAAJ4aJLcDAAAAT4nLly+blT333HOZ2oal+q5cuWJ1cvuhQ4ckSa+//rqGDh1qCDg5ODioePHi6t+/v1q2bKk333zTkGj59ddfq06dOhZnBY+OjtbKlSsNZWPGjFHTpk0t9sPNzU1ly5ZV2bJl1bFjR927dy/NFdUPHjyon3/+2VD2+uuva/DgwRZnxC5UqJC6deum1q1bq0+fPqYVgIODg/XZZ59p0qRJqbaXVOKMx/ny5dP06dPl5+dneN3JyUklS5ZUyZIlTWVt2rTRmDFjTEmue/fu1eXLl1WkSBGr2rxz547ZIIbUkqrXrFmjv/76y7Tt4OCg/v37691337U4w72fn5/69u2rNm3aqGfPnjp//rykB4Mffvrpp1RX096zZ4/mzZtnKGvYsKF+/vlnsxXN8+bNqzfffFOtWrXS22+/rWPHjpmuw6yQN29e1alTxxB8Wrp0qdXJ7YsXLzZsFytWTFWrVs3UPmam+fPnm5VZM9N44kCHmjVrauLEiWZBYjc3N9WsWdNQFh0drQEDBhgGH5QsWVJjx441u0ekB4N2GjZsqIYNG2ry5MmGIPp3332n+vXrpzowY9iwYYqIiDBtZ8uWTb/++qvq1Klj2M/FxUW1atVSrVq1tGDBAg0fPlz379/PlIE3qbl165YGDhxoSGx3d3fXyJEj1axZM4vH5M6dW/Xr11f9+vX16aefasWKFcqVK5dhn0aNGpn+vWrVKkNye8OGDc3OS1ru3bungQMHGgbF1KhRQ6NHj7Y4M7mPj49eeuklNWvWTN98841mz54t6cFgq08//VQbN240TWCQXHR0tIYOHWpYPcXHx0d//PGH2e9ZtmzZ1KBBA9WvX1+//PKLfvnlF127ds2m95ZeFStWVIMGDbR582ZD+eXLl/X111/rm2++UcmSJVWxYkVVqlRJFStWVMmSJVMdtGGrxJUfKleurF9//dWQcC49+J2pXr26pk6dqiFDhhgGhKxcuVKffPKJ2THJde/eXZ06dUp1cJarq6sqVqyoihUr6o033lDv3r11+PBhSQ8GHE6aNEnfffedTe8tcRCPl5eXfvvtN7NBZ46OjipSpIi6d+9uKJ8+fbrhnLi4uOjzzz9Xhw4dzNpwdHRUuXLlVK5cObVp00bvvvuu6W+Y/fv3648//lCPHj0s9i9x8ppEXbt21bBhwyzu6+TkpOLFi6t48eJq1qyZYmJitG7duhRX5gAAAAAAAACAp8WVK1e0c+dOQ1mFChXMYocjR440W0H73Xff1YABAwwxTQcHBxUuXFi9evVSmzZtzFbeHj9+vP73v/+pbNmyFvtjj2e/efPmNU3WvGvXLkNye548edKcyNmSrHoWbkl64oPpERsbqw8//NCQ2O7i4qKxY8ca4mDSg3ORGKdo3ry5evfubYoPxsXF6aOPPtLKlSttWnE98X2+9dZb+uijj8xi515eXmrZsmV6316ms7RCvTXnIfF9lipVStOmTVOePHkMr7u4uJg+26QGDx5sSHrNly+fxo4dazHh2s3NTbVr11bt2rW1dOlSDRkyxBRznDp1ql588UWz+pMaOXKkYQyKo6OjvvvuO7Vt29awX9Lr4MUXX1Tfvn0VHR2tY8eOpfk5ZERERIT69u1rSGx3cnLS0KFD9dprr1lcpdnHx0d16tRRnTp1NGjQIG3cuNFsTE1i7Fp6MGYnaXJ7zZo11b59e7u8H+nJHreSHsnHdXl5eVm1WIC9JF7TxYoV05QpU8zOi4ODg8qXL6/Ro0erWLFihoUk9uzZo5MnT6Y5Li0r7/EFCxaYTWxQrlw5TZgwweJYkKRjrTp06KCAgACzsWfpERMToxEjRhj6kngt9u7dO9VjiR0DAADgSWP+f2IAAAAAnkiWVtzNmTNnprZhqb7kq0qnpX79+ho+fHiqSXl+fn6aNGmSYZ+7d+9aTJyVHgxWSJrgWrly5RQT2y3JkSNHismfiUaNGmVIxuzcubNGjBiRZlDA19dXv/76qwoWLGgq++eff3Tq1Cmr+yc9CPZOmjTJYtKuJXnz5tXzzz9v2k5ISDBLlE7NsmXLDO+3atWqKl68uMV94+LiNGrUKEPZBx98oN69e1sMECZVuHBhTZ48WV5eXqayhQsXKjAwMMVjfv31VyUkJJi2S5UqpTFjxpgNTknK19dXU6dONUvczQrJg6uHDh0yBUVTExoaapiNWpJZYP1RsnDhQsOK6NKDZPOiRYtadXzhwoX122+/WT37+dKlSw0DCAoWLKgZM2ZYdY/07NlTXbt2NW1HRUXpzz//THH//fv3mw2ISpxwIzUdO3ZU37590+xPZpg8ebKCgoJM205OTpo8eXKa322JXFxc1K5dOz377LP26qIk6ffffzeshlKuXDlNnjzZYmJ7Uk5OTho+fLgaNmxoKrt7964WLVqU4jFr1641TQ4iPQgYT5w4MdUAu4ODg/r162fXQRyWfPfddykO4khISNDp06e1cOFCDR8+XG3atFH16tXVo0cPTZ482eIKDOlRsGBBTZ48OdUkdQcHBw0bNswwI3xMTIzZqueWfPDBBzatOvLMM89o0qRJhgE4K1as0L1796yuI6nRo0dbvZpKSEiIJkyYYCj7/vvvLQ7mSy5xgETS2fZnzJhhthpMoqQrNLi4uGjAgAFW9TFx/5deeinV3z8AAAAAAAAAeNLFxsZq6NChhgmAJalx48aG7YCAAIsrAH/wwQepxjTz58+v33//3RDDio2N1W+//ZbiMY/Ds9+sfBaeElvjg+mxbt06nTlzxlD25ZdfmiW2J1e7dm39+OOPhrLr16/bFHNP1KJFC33yySdpxs4ftm3btpklVubMmVPVqlWz6vgcOXJYTGxPyY4dOwyTK3h5een3339PdSXxRG3atDGs4pyQkKCpU6emuP+NGzfM3lv//v3TjL/Xq1dPX331VZr9yQzz5s0zm3xj5MiRev311y0mtifn4OCgxo0bWx0PywpP8riV9Lh165ZhO0eOHJlaf3p4eXlp6tSpaU440KdPH7OFT9asWZPqMVl5j0dFRWnMmDGGsjJlymjWrFmpLnKQVN68efXWW29ZtW9K7t+/r549exr+3nBxcdGoUaPSTGyXHo+/HwAAAABbPNpPQgAAAABkmrCwMLOyzA4CJw3kJEo6a3RanJ2dNWzYMKsCb+XKldMrr7xiKJs7d65hBd5EyRPdbEmes8aBAwe0b98+03bevHk1ePBgq4/38fExS3C1NON5al555ZU0ZzxOLnly5pIlSwxJ4alJHlB8+eWXU9x39erVhhWOy5Urp7ffftvqfhYuXFhvvvmmaTsqKkpLly61uO/ly5e1fft2Q9mwYcPk5uaWZju+vr4aOHCg1f3KLE2aNJG3t7ehLKX3l9Tq1asVGRlp2nZ0dHwkk9ujo6M1efJki7OWv/fee1bX88knn1i9ykFCQoKmT59uKBsyZIgh4TYt77//vllwOvmgp0Tz5s0zbPv7+6tVq1ZWtdOzZ08VLlzY6n6lR1BQkBYuXGgoe+edd+Tv72/Xdm0VHh6uOXPmmLYdHBz09ddfW33eHRwcNHjwYMPEJ8nPTVLJX2vbtq3Vg28+/vhji7959pIzZ07NmzdPL7zwglX7h4eHa+vWrRo9erQaN26s7t2769ChQxnqw6BBg6waQOHp6Wl2/R89ejRDbafE19fXMIguOjpa+/fvt7mehg0bql69elbvP2fOHMPfVY0aNdJLL71k9fGVKlUyfEa3bt3SP//8Y3HfkJAQ0799fHzsOoAPAAAAAAAAAJ40V69e1dtvv61du3YZyn19fc1WMp8zZ45iYmJM2zly5LA65lugQAG9//77hrINGzYYJvRN6nF49puVz8JTYkt8ML1mzZpl2Pb397d6kuPGjRubTZKQ2oTZlri4uGjo0KE2HZPV4uPjtWDBAr333ntm4zF69OhhVSxeehAbtjaxXZJZomrfvn1VokQJq4/v2rWrISF3/fr1KSYjL1iwwBALLlasmHr06GFVO7bEGNMrJibGLP7dsmVLtW7d2q7tZoUncdxKeiUfX5V8HMnD8O6771o1nsHR0VEdO3Y0lKUVI87Ke3zRokW6c+eOadvZ2VmjR4+2+29MUtevX9drr71mGNOUI0cOTZ8+3ep7+XH4+wEAAACwBcntAAAAwFPC0izomf2Q3t3d3ap2U1K3bl2bkjxfe+01w/a1a9fMZlWXzAM+x48ft5gEn14rVqwwbL/88ss2f7YvvviiYeb65Ctcp6VTp0427S89GICQdMXb69evm60+bcmRI0d0+vRp07aHh0eqqz8n/3xee+01m2edb9GihWE7pc9n8+bNhkDns88+q5o1a1rdTuvWrbM8QOjm5qbmzZsbypYuXZrmNZp85vhatWpZPaN0Zlq+fLnmzJlj+O+vv/7S5MmT9emnn6phw4YaPXq0WWJ406ZNzd53Sp555hk1aNDA6j4dO3ZM58+fN20XKFAgzdUVkvP29jYkEoeEhOjUqVMW900+ECf5d1NqXF1d03X/2mLLli2KiIgwbWfLlk3du3e3a5vp8d9//yk4ONi0Xa1aNZUtW9amOooWLary5cubts+cOWNYsT5RSEiI9u7dayiz5bzlzJnT7HvJ3nx9fTV16lRNnjxZNWrUsGoiGOnBZA/bt29X586d9fnnnxsG5lnLx8dHTZs2tXr/qlWrGraT3o+ZLfnM/elJ4rf1Hkz+u9a1a1eb27T2dy3pJAp3797VzZs3bW4LAAAAAAAAAJ5Et27dMotRzZkzRzNnztS4cePUo0cPNWvWTDt27DAc5+joqK+++sosnvvvv/8atlu1amWIpaalQ4cOhjrj4uK0detWi/s+Ds9+s/JZuCW2xgfT4/79+zpw4IChrEuXLjbV8cYbbxi2L168qMuXL1t9fMOGDW2aoNseNm3aZPFemjp1qkaMGKHGjRtr2LBhhonXJalKlSqGZN/UuLi42DRRe2BgoLZt22bazpYtm1nibFqcnZ0N8a34+HjDogVJbdq0ybDdsWNHubi4WN3W66+/blPfbHXo0CEFBAQYyqxZ5flx8CSOW0mv5HFcS2OwspKlhPXU2BIjzup7fO3atYbtJk2ayM/Pz6b2MuLYsWPq1KmT4ZotWLCg5syZY9OiBI/D3w8AAACALZzT3gUAAADAk8DV1dWszJZV1a2RNHkytXZTYmvy6bPPPquiRYvq0qVLprLDhw+rdOnShv2KFy8uLy8v3b9/X9KDAMrw4cP16aefZsostskDVnXr1rW5juzZs6to0aI6d+6cpAcJmWFhYfL09Ezz2Jw5c6pUqVI2t+nq6qpWrVpp5syZprJFixapdu3aqR73999/G7abNm2a4udoKXhk7crDSfn5+SlbtmymgPnBgwct7nf48GHDtq3XlJubm+rWratVq1bZ3MeMaNeunWEV6Rs3bmjXrl0pnosrV66Yfa4Pa9X2MWPG2HxM/fr1NWrUKKuTc6tVq2aY/CEte/bsMWw///zzVreVVNmyZQ3XwoEDB8ySrS9cuGBIyHZyclL9+vVtaqdRo0YaPXq0zf2zVvLvqP/97382DcbKKsnPW3q+S6UH5y0xwTkhIUGHDx82W5X78OHDhokwcufOrYoVK9rUTuPGjVNdGd5e6tWrp3r16unKlSvauHGjdu7cqf379+vevXupHpeQkKA5c+boypUrmjRpkk33VLVq1eTk5GT1/sknqkn8/bVVYGCgzp07p+DgYIWFhSkyMtJspYaLFy8atlNaBSclDg4OqlGjhtX7BwUFGSbScXNzs2nAQaJy5coZtlP6XatYsaJpAo34+Hi9//77Gj16tE2TAQEAAAAAAADAk+jixYv6/PPPbTrG1dXVlKybVHh4uNkkx02aNLGpbk9PT73wwguGBLoDBw5YTNZ71J/9ZvWzcEtsjQ+mx8GDBw0Tjru4uNicUO/v76+cOXMaJlvev3+/YTXh1NgyUbu9zJgxw+ZjKlWqpIkTJ1o9HqR06dI2xSf37t1riAlVrVrVqrETySWP7R44cMDs3o6IiDBbRMHWcQYNGzaUk5OT2YTvmWXXrl2G7XLlyunZZ5+1S1tZ7Ukct5JeySdUsDQGKyuVKlXKpvvWlhhxVt7j0dHRZufK2pXSM8M///yjgQMHGsbolS9fXpMmTbJ5cpNH/e8HAAAAwFYktwMAAABPCUsriYeGhmZqG5YCE7asYG7rCr2JxyRNbj958qTZPk5OTurYsaOmT59uKlu4cKHWrFmjZs2aqUGDBqpRo4Zy5Mhhc/vh4eFmgc79+/cbZtu1VtJV7uPj43X37l2rgjfpSWxP1L59e0OQcP369QoNDU0x6BcdHW2W+P3yyy+nWP+FCxcUEhJi2nZ2djab9dxaLi4upiBhYGCgYmNjzQY0JD//6b2msjq5vUqVKipevLguXLhgKluyZEmKAdulS5caAn2enp568cUX7d7PjMqbN6969epl0wrZku3XePKVm4OCgjRnzhyb6pBkNojp9u3bZvskv+ZKlChh8wzqxYsXl4eHR6ZPOJLo+PHjhu0qVarYpZ2MSn7erl69mq7zdvXqVcP2rVu3zPZJft6SD66yRnqOyUyFCxdWt27d1K1bN0nS5cuXdfToUR06dEi7du3SiRMnLB63detW/fzzz/roo4+sbqtgwYI29S35b5ctf28cO3ZMixcv1vr169M103zS3xxrFCxY0KaJbpJPjODl5aX58+fb1KYkw2A5yfL3iyS98sorpgEK0oP7pFmzZvrf//6nxo0bq1atWjafHwAAAAAAAAB4Gvn7+2vo0KF67rnnzF47e/asYmNjTdsODg7pigOUL1/ekNyePNaU6FF/9pvVz8ItyUgM3FrJz0+pUqVsmrxf+r9rZevWrSnWm5qseJ+ZycfHR2+88YbeffddmyYfyGi8Nzo6Ol1xw8TFBRJZugbPnDljuP89PDxUrFgxm9pxd3dXsWLFzNrLLI9LvDe9nrRxK+mVfHxVeicQzyz2jBFn5T1+/vx50zlLVLlyZZvbSo/Zs2frm2++MUx80aBBA/300082jadL9Kj//QAAAADYiuR2AAAA4CmRO3dus7Kks4dnhqSrFyfKmzev1ccXKFDA5jaTH2OpD5LUr18/7dq1S8eOHTOVhYaGauHChVq4cKEcHBzk5+enKlWqyN/fX3Xq1LFqhty7d++arSL7448/2vw+LAkODrZqRveMrMBctmxZPffcc6ZEz8jISK1atUqdOnWyuP+GDRsMn3GRIkVUvXr1FOtPHjiKjY21eQWFlISEhMjX19dQlvz8p+eaeliBnnbt2umnn34yba9bt06fffaZxYDWkiVLDNvNmze3OaHa3jw8POTt7a38+fOrYsWK8vf3V/369dMV2LX1Gr9z545he8OGDdqwYYPN7SZnaWXszLjmHB0dlT9/frsNdggMDDRsW7tSRFZLft4Sv58zyl7nLVeuXIaZ+R+2IkWKqEiRImrRooWkByvWzJgxQwsWLDAMyJGkP/74Q126dFH+/PmtqtvLy8umvjg6Ohq2kw9es+T+/fv6+uuvzSbvsFVYWJhN+9s6sU3y6/TOnTuZ8rtm6TqVHgxu6Ny5s+bNm2cqi42N1aZNm0yDbvLly6fKlSvL399ftWrVkp+fX4b7AwAAAAAAAACPK2dnZ2XPnl3e3t7y8/NTpUqV1LhxY5UsWTLFY5I/o82RI4fNz8YlqVChQqnWm+hRf/ab1c/CLclIDNxayfuT3jixtefdkvRMwJ9V3N3d5eXlpTx58qhChQqqXr26mjRpIjc3N5vrymi8d+/evdq7d6/N7SZnbdzQwcHB5roLFiyYZfHeJ22V5idt3Ep65cmTxzCmyZbvEnuw9XfQycnJsJ1ajDgr7/Hk94+Xl1emnbPUbNq0SYsXLzaUvf766xo6dKjZZ2WtR/3vBwAAAMBWJLcDAAAATwlLyYwnT55UkyZNMq0NS6umJw/kpsaWlVMTJQ+mpBTc8fDw0MyZMzV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       "text/plain": [
        "<Figure size 2000x1000 with 2 Axes>"
       ]
@@ -12960,9 +12675,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymc_examples_new",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "pymc_examples_new"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -12974,7 +12689,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.6"
   },
   "myst": {
    "substitutions": {
@@ -12983,5 +12698,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/generalized_linear_models/GLM-discrete-choice_models.myst.md b/examples/generalized_linear_models/GLM-discrete-choice_models.myst.md
index 18f089a66..f2defacb2 100644
--- a/examples/generalized_linear_models/GLM-discrete-choice_models.myst.md
+++ b/examples/generalized_linear_models/GLM-discrete-choice_models.myst.md
@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc_examples_new
+  display_name: Python 3 (ipykernel)
   language: python
-  name: pymc_examples_new
+  name: python3
 myst:
   substitutions:
     extra_dependencies: jax, jaxlib, numpyro
@@ -98,14 +98,14 @@ This assumption proves to be mathematically convenient because the difference be
 
 $$ \text{softmax}(u)_{j} = \frac{\exp(u_{j})}{\sum_{q=1}^{J}\exp(u_{q})} $$
 
-The model then assumes that decision maker chooses the option that maximises their subjective utility. The individual utility functions can be richly parameterised. The model is identified just when the utility measures of the alternatives are benchmarked against the fixed utility of the "outside good." The last quantity is fixed at 0. 
+The model then assumes that decision maker chooses the option that maximises their subjective utility. The individual utility functions can be richly parameterised. When contants are included, one alternative's constant must be fixed at 0 in order to normalise the overall level of the constants. 
 
 $$\begin{pmatrix}
 u_{gc}   \\
 u_{gr}   \\
 u_{ec}   \\
 u_{er}   \\
-0   \\
+u_{hp}   \\
 \end{pmatrix}
 $$
 
@@ -150,7 +150,9 @@ with pm.Model(coords=coords) as model_1:
     u1 = beta_ic * wide_heating_df["ic.er"] + beta_oc * wide_heating_df["oc.er"]
     u2 = beta_ic * wide_heating_df["ic.gc"] + beta_oc * wide_heating_df["oc.gc"]
     u3 = beta_ic * wide_heating_df["ic.gr"] + beta_oc * wide_heating_df["oc.gr"]
-    u4 = np.zeros(N)  # Outside Good
+    u4 = (
+        beta_ic * wide_heating_df["ic.hp"] + beta_oc * wide_heating_df["oc.hp"]
+    )  # np.zeros(N)  # Outside Good
     s = pm.math.stack([u0, u1, u2, u3, u4]).T
 
     ## Apply Softmax Transform
@@ -215,7 +217,7 @@ which suggests that there is almost twice the value accorded to the a unit reduc
 To assess overall model adequacy we can rely on the posterior predictive checks to see if the model can recover an approximation to the data generating process.
 
 ```{code-cell} ipython3
-idata_m1["posterior"]["p"].mean(dim=["chain", "draw", "obs"])
+idata_m1["posterior"]["p"].mean(dim=["chain", "draw", "obs"]).to_dataframe()["p"]
 ```
 
 ```{code-cell} ipython3
@@ -272,7 +274,9 @@ with pm.Model(coords=coords) as model_2:
     u1 = alphas[1] + beta_ic * wide_heating_df["ic.er"] + beta_oc * wide_heating_df["oc.er"]
     u2 = alphas[2] + beta_ic * wide_heating_df["ic.gc"] + beta_oc * wide_heating_df["oc.gc"]
     u3 = alphas[3] + beta_ic * wide_heating_df["ic.gr"] + beta_oc * wide_heating_df["oc.gr"]
-    u4 = np.zeros(N)  # Outside Good
+    u4 = (
+        0 + beta_ic * wide_heating_df["ic.hp"] + beta_oc * wide_heating_df["oc.hp"]
+    )  # Make one constant zero
     s = pm.math.stack([u0, u1, u2, u3, u4]).T
 
     ## Apply Softmax Transform