Changed pymc_experimental to pymc_extras

Author: fonnesbeck

examples/howto/marginalizing-models.ipynb

@@ -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);