For those of you interested in learning a bit more about discrete models, below are some fantastic resources to help you on your journey:

- Book: Murphy — Chapter 2 —
*Probability* - Book: Murphy — Chapter 3 —
*Generative Models for Discrete Data* - Book: Bishop — Chapter 2, Sections 2.1-2.2 —
*Probability Distributions* - Book: MacKay — Chapter 2 —
*Probability, Entropy, and Inference* - Book: MacKay — Chapter 3 —
*More About Inference* - Book: Mackay — Chapter 23 —
*Useful Probability Distributions*

- Video: Iain Murray — Introduction to Machine Learning, Part 2
- Metacademy: Bayesian Parameter Estimation
- Metacademy: Dirichlet Distribution

### Book reference for above:

Machine Learning: A Probabilistic Perspective Kevin P. Murphy, MIT Press, 2012.

Pattern Recognition and Machine Learning Christopher M. Bishop, Springer, 2006. An excellent and affordable book on machine learning, with a Bayesian focus. It covers fewer topics than the Murphy book, but goes into more depth on the topics it covers.

David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press.