Updated multilevel modeling to v4

.pre-commit-config.yaml

@@ -69,7 +69,6 @@ repos:
                examples/case_studies/blackbox_external_likelihood.ipynb|
                examples/case_studies/blackbox_external_likelihood_numpy.ipynb|
                examples/case_studies/item_response_nba.ipynb|
-               examples/case_studies/multilevel_modeling.ipynb|
                examples/diagnostics_and_criticism/sampler-stats.ipynb|
                examples/gaussian_processes/GP-MaunaLoa2.ipynb|
                examples/generalized_linear_models/GLM-logistic.ipynb|

examples/case_studies/multilevel_modeling.ipynb

@@ -4,10 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "(notebook_name)=\n",
     "# A Primer on Bayesian Methods for Multilevel Modeling\n",
     "\n",
     ":::{post} 27 February, 2022\n",
-    ":tags: hierarchical, pymc.Data, pymc.Deterministic, pymc.Exponential, pymc.LKJCholeskyCov, pymc.Model, pymc.MvNormal, pymc.Normal\n",
+    ":tags: hierarchical, Data, Deterministic, Exponential, LKJCholeskyCov, Model, MvNormal, Normal\n",
     ":category: intermediate\n",
     ":author: Chris Fonnesbeck, Colin Carroll, Alex Andorra, Oriol Abril, Farhan Reynaldo\n",
     ":::"
@@ -570,7 +571,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You may be wondering why we are using the `pm.Data` container above even though the variable `floor_idx` is not an observed variable nor a parameter of the model. As you'll see, this will make our lives much easier when we'll plot and diagnose our model. In short, this will tell [ArviZ](https://arviz-devs.github.io/arviz/index.html) that `floor_idx` is information used by the model to index variables. ArviZ will thus include `floor_idx` as a variable in the `constant_data` group of the resulting [`InferenceData`](https://arviz-devs.github.io/arviz/notebooks/XarrayforArviZ.html) object. Moreover, including `floor_idx` in the `InferenceData` object makes sharing and reproducing analysis much easier, all the data needed to analyze or rerun the model is stored there."
+    "You may be wondering why we are using the `pm.Data` container above even though the variable `floor_idx` is not an observed variable nor a parameter of the model. As you'll see, this will make our lives much easier when we'll plot and diagnose our model. In short, this will tell {doc}`Arviz <arviz:index>` that `floor_idx` is information used by the model to index variables. ArviZ will thus include `floor_idx` as a variable in the `constant_data` group of the resulting {ref}`InferenceData <xarray_for_arviz>` object. Moreover, including `floor_idx` in the `InferenceData` object makes sharing and reproducing analysis much easier, all the data needed to analyze or rerun the model is stored there."
    ]
   },
   {
@@ -4222,12 +4223,23 @@
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
   }
  ],
  "metadata": {
   "celltoolbar": "Raw Cell Format",
+  "interpreter": {
+   "hash": "5429d053af7e221df99a6f00514f0d50433afea7fb367ba3ad570571d9163dca"
+  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.10 ('pymc-dev-py39')",
    "language": "python",
    "name": "python3"
   },

myst_nbs/case_studies/multilevel_modeling.myst.md

@@ -6,15 +6,16 @@ jupytext:
     format_version: 0.13
     jupytext_version: 1.13.7
 kernelspec:
-  display_name: Python 3 (ipykernel)
+  display_name: Python 3.9.10 ('pymc-dev-py39')
   language: python
   name: python3
 ---
 
+(notebook_name)=
 # A Primer on Bayesian Methods for Multilevel Modeling
 
 :::{post} 27 February, 2022
-:tags: hierarchical, pymc.Data, pymc.Deterministic, pymc.Exponential, pymc.LKJCholeskyCov, pymc.Model, pymc.MvNormal, pymc.Normal
+:tags: hierarchical, Data, Deterministic, Exponential, LKJCholeskyCov, Model, MvNormal, Normal
 :category: intermediate
 :author: Chris Fonnesbeck, Colin Carroll, Alex Andorra, Oriol Abril, Farhan Reynaldo
 :::
@@ -167,7 +168,7 @@ with pm.Model(coords=coords, rng_seeder=RANDOM_SEED) as pooled_model:
 pm.model_to_graphviz(pooled_model)
 ```
 
-You may be wondering why we are using the `pm.Data` container above even though the variable `floor_idx` is not an observed variable nor a parameter of the model. As you'll see, this will make our lives much easier when we'll plot and diagnose our model. In short, this will tell [ArviZ](https://arviz-devs.github.io/arviz/index.html) that `floor_idx` is information used by the model to index variables. ArviZ will thus include `floor_idx` as a variable in the `constant_data` group of the resulting [`InferenceData`](https://arviz-devs.github.io/arviz/notebooks/XarrayforArviZ.html) object. Moreover, including `floor_idx` in the `InferenceData` object makes sharing and reproducing analysis much easier, all the data needed to analyze or rerun the model is stored there.
+You may be wondering why we are using the `pm.Data` container above even though the variable `floor_idx` is not an observed variable nor a parameter of the model. As you'll see, this will make our lives much easier when we'll plot and diagnose our model. In short, this will tell {doc}`Arviz <arviz:index>` that `floor_idx` is information used by the model to index variables. ArviZ will thus include `floor_idx` as a variable in the `constant_data` group of the resulting {ref}`InferenceData <xarray_for_arviz>` object. Moreover, including `floor_idx` in the `InferenceData` object makes sharing and reproducing analysis much easier, all the data needed to analyze or rerun the model is stored there.
 
 +++
 
@@ -1134,3 +1135,6 @@ mcelreath2018statistical
 %load_ext watermark
 %watermark -n -u -v -iv -w
 ```
+
+:::{include} ../page_footer.md
+:::