Recently I have been looking for a good short resource for the fundamental Bayesian inference. There are tons of good relevant materials online, unfortunately, some are too long, some are too theoretic. Come on, I want to find something intuitive, making sense and understandable and readable within 30 minutes! After a couple of hours wandering on internet and reading some good relevant books, I decided to pick the best 3 books, of course in my opinion, that contributes to my understanding the most within half an hour.
- Bayesian Data Analysis by Andrew Gelman et al. — If you understand the concept of Bayesian framework and just want to review Bayesian inference, then the first pages of Chapter2 are sufficient. Good examples can be found in section 2.6.
- Bayesian core: a practical approach to computational Bayesian statistics by Jean-Michel Marin, Christian P. Robert — The book emphasizes the important concepts and has a lot of examples necessary to understand the basic ideas.
- Bayesian Field Theory by Jörg C. Lemm — This book provides great perspective of Bayesian framework to learning, information theory and physics. There are very a lot of figures and graphical models making the book easy to follow.