chapter 6 mc -> pm

Author: cebe

Chapter6_Priorities/Priors.ipynb

@@ -236,14 +236,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pymc as mc\n",
+      "import pymc as pm\n",
       "\n",
       "n = 4\n",
       "for i in range( 10 ):\n",
       "    ax = plt.subplot( 2, 5, i+1)\n",
       "    if i >= 5:\n",
       "        n = 15\n",
-      "    plt.imshow( mc.rwishart( n+1, np.eye(n) ), interpolation=\"none\", \n",
+      "    plt.imshow( pm.rwishart( n+1, np.eye(n) ), interpolation=\"none\", \n",
       "                cmap = plt.cm.hot ) \n",
       "    \n",
       "    ax.axis(\"off\")\n",
@@ -1056,15 +1056,15 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pymc as mc\n",
+      "import pymc as pm\n",
       "\n",
       "n_observations = 100 #we will truncate the the most recent 100 days.\n",
       "\n",
       "prior_mu = np.array( [ x[0] for x in expert_prior_params.values() ] )\n",
       "prior_std = np.array( [ x[1] for x in expert_prior_params.values() ] )\n",
       "\n",
-      "inv_cov_matrix = mc.Wishart( \"inv_cov_matrix\", n_observations, diag(prior_std**2) )\n",
-      "mu = mc.Normal( \"returns\", prior_mu, 1, size = 4 )"
+      "inv_cov_matrix = pm.Wishart( \"inv_cov_matrix\", n_observations, diag(prior_std**2) )\n",
+      "mu = pm.Normal( \"returns\", prior_mu, 1, size = 4 )"
      ],
      "language": "python",
      "metadata": {},
@@ -1183,10 +1183,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "obs = mc.MvNormal( \"observed returns\", mu, inv_cov_matrix, observed = True, value = returns )\n",
+      "obs = pm.MvNormal( \"observed returns\", mu, inv_cov_matrix, observed = True, value = returns )\n",
       "\n",
-      "model = mc.Model( [obs, mu, inv_cov_matrix] )\n",
-      "mcmc = mc.MCMC()\n",
+      "model = pm.Model( [obs, mu, inv_cov_matrix] )\n",
+      "mcmc = pm.MCMC()\n",
       "\n",
       "mcmc.sample( 150000, 100000, 3 )"
      ],
@@ -1435,7 +1435,7 @@
       "beta = stats.beta\n",
       "\n",
       "x = np.linspace(0.00, 1, 125)\n",
-      "data = mc.rbernoulli(p, size=500)\n",
+      "data = pm.rbernoulli(p, size=500)\n",
       "\n",
       "figure()\n",
       "for i,N in enumerate([0,4,8, 32,64, 128, 500]):\n",