| title | Classwork, Resources and Readings |
|---|---|
| shorttitle | Resources |
| layout | default |
| nav_include | 4 |
| noline | 1 |
The course is mostly self-contained, but reading a textbook will help you with your understanding.
- Bayesian Methods for Hackers by Cameron Davidson-Pilon. Scroll down to the PyMC3 part in the README.
- Model Based Machine Learning
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath.
It is strongly suggested you buy the above book.
- Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. 3rd Edition.
We will not be using R or Stan, but the principles remain the same.
- Notes on Probability and Statistics from Shalizi
- Skim and keep for reference: the first 3 chapters of Wasserman's All of Statistics