pymc-devs · OriolAbril · Nov 24, 2021 · Sep 5, 2021 · Nov 13, 2021 · Nov 14, 2021
diff --git a/docs/source/Advanced_usage_of_Aesara_in_PyMC.rst b/docs/source/Advanced_usage_of_Aesara_in_PyMC.rst
@@ -1,5 +1,5 @@
 :orphan:
-
+(Advanced_usage_of_Aesara_in_PyMC)
 ..
     _referenced in docs/source/notebooks/table_of_contents_tutorials.js
 

diff --git a/docs/source/community.md b/docs/source/community.md
@@ -1,3 +1,4 @@
+(community)=
 # Community
 
 :::{list-table}

diff --git a/docs/source/glossary.md b/docs/source/glossary.md
@@ -1,3 +1,4 @@
+(glossary)=
 # Glossary
 
 A glossary of common terms used throughout the PyMC documentation and examples.
@@ -77,9 +78,9 @@ GLM
 
 [Maximum a Posteriori](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation)
 [MAP](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation)
-  It is a point-estimate of an unknown quantity, that equals the mode of the posterior distribution. 
+  It is a point-estimate of an unknown quantity, that equals the mode of the posterior distribution.
 
-  If the prior distribution is a flat distribution, the MAP method is numerically equivalent to the {term}`Maximum Likelihood Estimate` (MLE). 
+  If the prior distribution is a flat distribution, the MAP method is numerically equivalent to the {term}`Maximum Likelihood Estimate` (MLE).
   When the prior is not flat the MAP estimation can be seen as a regularized version of the MLE.
 
   - For a concise comparison between {term}`MLE` and {term}`MAP`, consider Deep Learning by Ian Goodfellow, chapter 5.6.1 or [Machine Learning: a Probabilistic Perspective](https://probml.github.io/pml-book/book1.html) by Kevin Murphy.

diff --git a/docs/source/index.md b/docs/source/index.md
@@ -1,7 +1,7 @@
 ---
 sd_hide_title: true
 ---
-
+(index)=
 # PyMC Documentation
 
 :::{card}

diff --git a/docs/source/installation.md b/docs/source/installation.md
@@ -1,3 +1,4 @@
+(installation)=
 # Installation
 
 To install PyMC, select the operating system where you want to perform the installation.

diff --git a/docs/source/learn/books.md b/docs/source/learn/books.md
@@ -1,3 +1,4 @@
+(books)=
 # Books
 
 TODO add books
diff --git a/docs/source/learn/examples.md b/docs/source/learn/examples.md
@@ -1,3 +1,4 @@
+(examples)=
 # Notebooks
 
 TODO add "core" notebooks:

diff --git a/docs/source/learn/videos_and_podcasts.md b/docs/source/learn/videos_and_podcasts.md
@@ -1,3 +1,4 @@
+(videos_and_podcasts)=
 # Videos and Podcasts
 
 :::{card} PyMC Developers Youtube channel

diff --git a/docs/source/learning.md b/docs/source/learning.md
@@ -30,15 +30,15 @@ A great introductory book written by a maintainer of PyMC. It provides a hands-o
 ### {octicon}`mortar-board;1em;sd-text-info` Tutorial notebooks
 
 #### Getting started
-The {ref}`examples/getting_started` notebook in our documentation shows the PyMC 4.0 code in action
+The {ref}`getting_started` notebook in our documentation shows the PyMC 4.0 code in action
 
 #### General Linear Models: Linear regression
 
-The {ref}`examples/GLM_linear` notebook provides a gentle introduction to Bayesian linear regression and how it differs from the frequentist approach, and showcases how to implement it using PyMC.
+The {ref}`GLM_linear` notebook provides a gentle introduction to Bayesian linear regression and how it differs from the frequentist approach, and showcases how to implement it using PyMC.
 
 #### Comparing models
 
-The {ref}`examples/model_comparison` notebook demonstrates the use of model comparison criteria in PyMC.
+The {ref}`model_comparison` notebook demonstrates the use of model comparison criteria in PyMC.
 
 #### Validating a model using prior and posterior predictive checks