Update documentation

Author: AllenDowney

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@@ -8,7 +8,9 @@
     "# The All-Knowing Cube of Probability\n",
     "\n",
     "> This example uses array computations to explore the concept of conjugate distributions.\n",
-    "It is an extension of *[Think Bayes](https://greenteapress.com/wp/think-bayes/)*, [Chapter 18](https://allendowney.github.io/ThinkBayes2/chap18.html), which explains how to use conjugate priors to do Bayesian updates with very little computation. \n",
+    "It is an extension of *[Think Bayes](https://greenteapress.com/wp/think-bayes/)*, [Chapter 18](https://allendowney.github.io/ThinkBayes2/chap18.html), which explains how to use conjugate priors to do Bayesian updates with very little computation.\n",
+    "\n",
+    "[Click here to run this notebook on Colab](https://colab.research.google.com/github/AllenDowney/ThinkBayes2/blob/master/examples/beta_binomial.ipynb)\n",
     "\n",
     "The all-knowing cube of probability is an 3-D array that contains the past, the present, and the probabilistic future.\n",
     "\n",

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@@ -7,21 +7,6 @@
     "# MCMC"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "source": [
-    "Think Bayes, Second Edition\n",
-    "\n",
-    "Copyright 2020 Allen B. Downey\n",
-    "\n",
-    "License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -109,7 +94,7 @@
     "But the problems we can solve this way tend to be the same ones we can solve with grid algorithms.\n",
     "\n",
     "For problems with more than a few parameters, the most powerful tool we have is MCMC, which stands for \"Markov chain Monte Carlo\".\n",
-    "In this context, \"Monte Carlo\" refers to to methods that generate random samples from a distribution.\n",
+    "In this context, \"Monte Carlo\" refers to methods that generate random samples from a distribution.\n",
     "Unlike grid methods, MCMC methods don't try to compute the posterior distribution; they sample from it instead.\n",
     "\n",
     "It might seem strange that you can generate a sample without ever computing the distribution, but that's the magic of MCMC.\n",
@@ -3659,6 +3644,21 @@
     "    az.plot_posterior(trace9)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove-cell"
+    ]
+   },
+   "source": [
+    "Think Bayes, Second Edition\n",
+    "\n",
+    "Copyright 2020 Allen B. Downey\n",
+    "\n",
+    "License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3670,7 +3670,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3684,7 +3684,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,