remove references to pymc3

Author: ferrine

examples/variational_inference/GLM-hierarchical-advi-minibatch.ipynb

@@ -7,7 +7,7 @@
     "# GLM: Mini-batch ADVI on hierarchical regression model\n",
     "\n",
     ":::{post} Sept 23, 2021\n",
-    ":tags: generalized linear model, hierarchical model, pymc3.Minibatch, pymc3.Model, pymc3.NUTS, pymc3.Normal, pymc3.Uniform, variational inference\n",
+    ":tags: generalized linear model, hierarchical model, pymc.Minibatch, pymc.Model, pymc.NUTS, pymc.Normal, pymc.Uniform, variational inference\n",
     ":category: intermediate\n",
     ":::"
    ]

examples/variational_inference/empirical-approx-overview.ipynb

@@ -6,6 +6,11 @@
    "source": [
     "# Empirical Approximation overview\n",
     "\n",
+    ":::\n",
+    ":tags: pymc.Empirical, variational inference\n",
+    ":category: intermediate\n",
+    ":::\n",
+    "\n",
     "For most models we use sampling MCMC algorithms like Metropolis or NUTS. In PyMC3 we got used to store traces of MCMC samples and then do analysis using them. There is a similar concept for the variational inference submodule in PyMC3: *Empirical*. This type of approximation stores particles for the SVGD sampler. There is no difference between independent SVGD particles and MCMC samples. *Empirical* acts as a bridge between MCMC sampling output and full-fledged VI utils like `apply_replacements` or `sample_node`. For the interface description, see [variational_api_quickstart](variational_api_quickstart.ipynb). Here we will just focus on `Emprical` and give an overview of specific things for the *Empirical* approximation"
    ]
   },

myst_nbs/variational_inference/GLM-hierarchical-advi-minibatch.myst.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.13.7
+    jupytext_version: 1.13.8
 kernelspec:
   display_name: pymc
   language: python
@@ -14,7 +14,7 @@ kernelspec:
 # GLM: Mini-batch ADVI on hierarchical regression model
 
 :::{post} Sept 23, 2021
-:tags: generalized linear model, hierarchical model, pymc3.Minibatch, pymc3.Model, pymc3.NUTS, pymc3.Normal, pymc3.Uniform, variational inference
+:tags: generalized linear model, hierarchical model, pymc.Minibatch, pymc.Model, pymc.NUTS, pymc.Normal, pymc.Uniform, variational inference
 :category: intermediate
 :::
 

myst_nbs/variational_inference/empirical-approx-overview.myst.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.13.7
+    jupytext_version: 1.13.8
 kernelspec:
   display_name: pymc
   language: python
@@ -13,6 +13,11 @@ kernelspec:
 
 # Empirical Approximation overview
 
+:::
+:tags: pymc.Empirical, variational inference
+:category: intermediate
+:::
+
 For most models we use sampling MCMC algorithms like Metropolis or NUTS. In PyMC3 we got used to store traces of MCMC samples and then do analysis using them. There is a similar concept for the variational inference submodule in PyMC3: *Empirical*. This type of approximation stores particles for the SVGD sampler. There is no difference between independent SVGD particles and MCMC samples. *Empirical* acts as a bridge between MCMC sampling output and full-fledged VI utils like `apply_replacements` or `sample_node`. For the interface description, see [variational_api_quickstart](variational_api_quickstart.ipynb). Here we will just focus on `Emprical` and give an overview of specific things for the *Empirical* approximation
 
 ```{code-cell} ipython3