PyMC 4 -> PyMC.

Author: twiecki

Chapter3_MCMC/Ch3_IntroMCMC_PyMC_current.ipynb

@@ -517,14 +517,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Please notice that the early version of PyMC use `ElemwiseCategorical()` for categorical variable. But in PyMC 4, it was deprecated. But new PyMC provieds a new functions to do the categorical sampling, the `CategoricalGibbsMetropolis` optimized for categorical variables."
+    "Please notice that the early version of PyMC use `ElemwiseCategorical()` for categorical variable. But in PyMC, it was deprecated. But new PyMC provieds a new functions to do the categorical sampling, the `CategoricalGibbsMetropolis` optimized for categorical variables."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are another interesting thing with new PyMC 4. The PyMC 4 use a powerful new sampling principle be called Hamiltonian Monte Carlo (HMC). We'll not talk too much about it here, since it's a complex physical principle. But we should know that [HMC and NUTS take advantage of gradient information from the likelihood to achieve much faster convergence than traditional sampling methods, especially for larger models. ](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#pymc-overview)\n",
+    "There are another interesting thing with new PyMC. The PyMC use a powerful new sampling principle be called Hamiltonian Monte Carlo (HMC). We'll not talk too much about it here, since it's a complex physical principle. But we should know that [HMC and NUTS take advantage of gradient information from the likelihood to achieve much faster convergence than traditional sampling methods, especially for larger models. ](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#pymc-overview)\n",
     "\n"
    ]
   },