## Books

The course is mostly self-contained, but reading a textbook will help you with your understanding.

### Required:

- Bayesian Methods for Hackers by Cameron Davidson-Pilon. Scroll down to the PyMC3 part in the README.
- Model Based Machine Learning

### Highly Recommended

- 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.

### Other useful books and sources

## Papers and other readings

### Week 1 Reading

- Notes on Probability and Statistics from Shalizi
- Skim and keep for reference: the first 3 chapters of Wasserman’s All of Statistics