Fix broken links

pymc-devs · Jun 4, 2024 · 08f576c · 08f576c
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diff --git a/docs/source/404.md b/docs/source/404.md
@@ -10,4 +10,3 @@ Click on the navigation bar on top of the page to go to the right section
 of the default docs, or alternatively:
 
 * Go to the current [PyMC website homepage](https://www.pymc.io/)
-* Go to the homepage of [PyMC 3.x documentation](https://www.pymc.io/projects/docs/en/v3/)
diff --git a/docs/source/contributing/developer_guide.md b/docs/source/contributing/developer_guide.md
@@ -546,7 +546,7 @@ And the transition kernel (i.e., ``.astep()``) takes an array as input and outpu
 For example, see the [MH sampler](https://github.com/pymc-devs/pymc/blob/89f6fcf751774fb50016561dc448a87fba7ed3aa/pymc/step_methods/metropolis.py#L235-L289).
 
 This is of course very different compared to the transition kernel in e.g. TFP, which is a tenor in tensor out function.
-Moreover, transition kernels in TFP do not flatten the tensors, see eg docstring of [tensorflow\_probability/python/mcmc/random\_walk\_metropolis.py](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/mcmc/random_walk_metropolis.py):
+Moreover, transition kernels in TFP do not flatten the tensors, see eg docstring of [tensorflow\_probability/python/mcmc/random\_walk\_metropolis.py](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/python/mcmc/random_walk_metropolis.py):
 
 ```python
         new_state_fn: Python callable which takes a list of state parts and a
@@ -648,7 +648,7 @@ As in the batch random generation, we want to generate (n\_sample, ) + RV.shape
 In some cases, where we broadcast RV1 and RV2 to create a RV3 that has one more batch shape, we get error (even worse, wrong answer with silent error).
 
 The good news is, we are fixing these errors with the amazing works from [lucianopaz](https://github.com/lucianopaz) and others.
-The challenge and some summary of the solution could be found in Luciano's [blog post](https://lucianopaz.github.io/2019/08/19/pymc-shape-handling/)
+The challenge and some summary of the solution could be found in Luciano's [blog post](https://lucianopaz.github.io/2019/08/19/pymc3-shape-handling/)
 
 ```python
 with pm.Model() as m:
@@ -666,8 +666,8 @@ There are also other error related random sample generation (e.g., [Mixture is c
 
 ### Extending PyMC
 -  Custom Inference method
-    -  [Inferencing Linear Mixed Model with EM.ipynb](https://github.com/junpenglao/Planet_Sakaar_Data_Science/blob/master/Ports/Inferencing%20Linear%20Mixed%20Model%20with%20EM.ipynb)
-    -  [Laplace approximation in  pymc.ipynb](https://github.com/junpenglao/Planet_Sakaar_Data_Science/blob/master/Ports/Laplace%20approximation%20in%20pymc.ipynb)
+    -  [Inferencing Linear Mixed Model with EM.ipynb](https://github.com/junpenglao/Planet_Sakaar_Data_Science/blob/main/Ports/Inferencing%20Linear%20Mixed%20Model%20with%20EM.ipynb)
+    -  [Laplace approximation in  pymc.ipynb](https://github.com/junpenglao/Planet_Sakaar_Data_Science/blob/main/Ports/Laplace%20approximation%20in%20pymc3.ipynb)
 -  Connecting it to other library within a model
     -  Using "black box" likelihood function by creating a custom PyTensor Op.
     -  Using emcee

diff --git a/docs/source/learn/books.md b/docs/source/learn/books.md
@@ -16,7 +16,7 @@ Hands on approach with PyMC and ArviZ focusing on the practice of applied statis
 :::
 
 :::{grid-item-card} Bayesian Methods for Hackers
-:img-top: https://camo.githubusercontent.com/4a0aca82ca82efab71747d00db30f3a68de98e82/687474703a2f2f692e696d6775722e636f6d2f36444b596250622e706e673f31
+:img-top: https://www.pearson.com/hipassets/assets/hip/images/bigcovers/0133902838.jpg
 By Cameron Davidson-Pilon
 
 The "hacker" in the title  means learn-as-you-code. This hands-on introduction teaches intuitive definitions of the Bayesian approach to statistics, worklflow and decision-making by applying them using PyMC.
@@ -28,7 +28,7 @@ The "hacker" in the title  means learn-as-you-code. This hands-on introduction t
 :::
 
 :::{grid-item-card} Bayesian Analysis with Python
-:img-top: https://aloctavodia.github.io/img/BAP.jpg
+:img-top: https://aloctavodia.github.io/img/BAP.png
 
 By Osvaldo Martin
 
@@ -55,7 +55,7 @@ Principled introduction to Bayesian data analysis, with practical exercises. The
 :::
 
 :::{grid-item-card} Statistical Rethinking
-:img-top: http://xcelab.net/rm/wp-content/uploads/2012/01/9781482253443-191x300.jpg
+:img-top: https://xcelab.net/rm/sr2edcover-1-187x300.png
 
 By Richard McElreath