Adapt and run blackbox likelihood tutorial with numpy (pymc-devs#28)

* Add numpy version of blackbox likelihood notebook * edit titles to distinguish the two different versions * update numpy version of blackbox likelihood nb * add target to custom distribution notebook * add tags * fix code block syntax * rerun and fix section headings * move section to right place * review changes and rename * address more review comments
martiningram · Dec 19, 2021 · 3290b46 · 3290b46
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diff --git a/examples/case_studies/blackbox_external_likelihood.ipynb b/examples/case_studies/blackbox_external_likelihood.ipynb
@@ -4,7 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using a \"black box\" likelihood function"
+    "(blackbox_external_likelihood)=\n",
+    "# Using a \"black box\" likelihood function (Cython)\n",
+    "\n",
+    ":::{note}\n",
+    "This notebook in part of a set of two twin notebooks that perform the exact same task, this one\n",
+    "uses Cython whereas {ref}`this other one <blackbox_external_likelihood_numpy>` uses NumPy\n",
+    ":::"
    ]
   },
   {
@@ -818,7 +824,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -832,7 +838,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

diff --git a/examples/case_studies/blackbox_external_likelihood_numpy.ipynb b/examples/case_studies/blackbox_external_likelihood_numpy.ipynb
diff --git a/examples/pymc3_howto/custom_distribution.ipynb b/examples/pymc3_howto/custom_distribution.ipynb
@@ -4,6 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "(custom_distribution)=\n",
     "# Defining a Custom Distribution in PyMC3"
    ]
   },
@@ -534,7 +535,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -548,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.1"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

diff --git a/examples/pymc3_howto/profiling.ipynb b/examples/pymc3_howto/profiling.ipynb
@@ -4,6 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "(profiling)=\n",
     "# Profiling\n",
     "Sometimes computing the likelihood is not as fast as we would like. Theano provides handy profiling tools which are wrapped in PyMC3 by `model.profile`. This function returns a `ProfileStats` object conveying information about the underlying Theano operations. Here we'll profile the likelihood and gradient for the stochastic volatility example.\n",
     "\n",
@@ -315,7 +316,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -329,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

diff --git a/examples/table_of_contents_tutorials.js b/examples/table_of_contents_tutorials.js
@@ -16,6 +16,7 @@ Gallery.contents = {
     "samplers/DEMetropolisZ_tune_drop_fraction": "Deep dives",
     "case_studies/factor_analysis": "Deep dives",
     "case_studies/blackbox_external_likelihood": "How-To",
+    "case_studies/blackbox_external_likelihood_numpy": "How-To",
     "pymc3_howto/profiling": "How-To",
     "pymc3_howto/howto_debugging": "How-To",
     "diagnostics_and_criticism/model_averaging": "How-To",