Premise

This project allows me to implement different Bayesian modelling techniques I have seen through class and my own research. In addition, I hope it serves as educational material for others. I have found that the best way to learn is through practice, and so this project is expected to help me master the material I need to undertake my research projects.

The notes and examples I share will be heavily based on work I found on the following pages:

• https://vioshyvo.github.io/Bayesian_inference/index.html{.uri}

• https://matheusfacure.github.io/python-causality-handbook/landing-page.html

• https://www.math.mcgill.ca/dstephens/556/

• https://www.math.mcgill.ca/dstephens/557/

• https://www.math.mcgill.ca/yyang/comp.html

• https://www.math.mcgill.ca/dstephens/559/{.uri}

Gibbs Sampler Simulation to estimate Multivariate Normal Distribution (MCMC)

Links to Notebooks

Links to the different notebooks I make will be stored here:

TODO

1. Add citations to each topic

Missing topics

• Dirichlet Processes
• Variational Inference
• GLMs