Extras update (#752)

* Draft update of BNN notebook

* Pre-commit fixes

* Address reviewer comments

* Additional edits

* Removed bivariate example; updated formatting

* Updated GEV

* Changed pymc_experimental to pymc_extras

---------

Co-authored-by: Chris Fonnesbeck <cfonnesbeck@phillies.com>

Author: fonnesbeck

examples/case_studies/GEV.ipynb

@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc4-dev
+  display_name: default
   language: python
-  name: pymc4-dev
+  name: python3
 ---
 
 # Generalized Extreme Value Distribution
@@ -42,7 +42,7 @@ import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pymc as pm
-import pymc_experimental.distributions as pmx
+import pymc_extras.distributions as pmx
 import pytensor.tensor as pt
 
 from arviz.plots import plot_utils as azpu
@@ -230,7 +230,3 @@ az.plot_pair(idata, var_names=["μ", "σ", "ξ"], kind="kde", marginals=True, di
 %load_ext watermark
 %watermark -n -u -v -iv -w -p pytensor,arviz
 ```
-
-```{code-cell} ipython3
-
-```

examples/case_studies/GEV.myst.md

@@ -5,9 +5,9 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: pymc-dev
+  display_name: default
   language: python
-  name: pymc-dev
+  name: python3
 myst:
   substitutions:
     extra_dependencies: pymc-experimental
@@ -53,7 +53,7 @@ import pytensor.tensor as pt
 :::
 
 ```{code-cell} ipython3
-import pymc_experimental as pmx
+import pymc_extras as pmx
 ```
 
 ```{code-cell} ipython3
@@ -84,7 +84,7 @@ with pmx.MarginalModel() as explicit_mixture:
 plt.hist(pm.draw(y, draws=2000, random_seed=rng), bins=30, rwidth=0.9);
 ```
 
-The other way is where we use the built-in {class}`NormalMixture <pymc.NormalMixture>` distribution. Here the mixture assignment is not an explicit variable in our model. There is nothing unique about the first model other than we initialize it with {class}`pmx.MarginalModel <pymc_experimental.MarginalModel>` instead of {class}`pm.Model <pymc.model.core.Model>`. This different class is what will allow us to marginalize out variables later.
+The other way is where we use the built-in {class}`NormalMixture <pymc.NormalMixture>` distribution. Here the mixture assignment is not an explicit variable in our model. There is nothing unique about the first model other than we initialize it with {class}`pmx.MarginalModel <pymc_extras.MarginalModel>` instead of {class}`pm.Model <pymc.model.core.Model>`. This different class is what will allow us to marginalize out variables later.
 
 ```{code-cell} ipython3
 with pm.Model() as prebuilt_mixture:
@@ -121,7 +121,7 @@ az.summary(idata)
 
 As we can see, the `idx` variable is gone now. We also were able to use the NUTS sampler, and the ESS has improved.
 
-But {class}`MarginalModel <pymc_experimental.MarginalModel>` has a distinct advantage. It still knows about the discrete variables that were marginalized out, and we can obtain estimates for the posterior of `idx` given the other variables. We do this using the {meth}`recover_marginals <pymc_experimental.MarginalModel.recover_marginals>` method.
+But {class}`MarginalModel <pymc_extras.MarginalModel>` has a distinct advantage. It still knows about the discrete variables that were marginalized out, and we can obtain estimates for the posterior of `idx` given the other variables. We do this using the {meth}`recover_marginals <pymc_extras.MarginalModel.recover_marginals>` method.
 
 ```{code-cell} ipython3
 explicit_mixture.recover_marginals(idata, random_seed=rng);