Most of the necessary readings in the course are covered in the lessons and lecture notes. I will often refer to resources for further reading.
Papers
- Betancourt, A Conceptual Introduction to Hamiltonian Monte Carlo, 2018
- Vehtari, Gelman, and Gabry, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC, 2016
- Talts, et al., Validating Bayesian Inference Algorithms with Simulation-Based Calibration, 2018
Web-based resources
- Michael Betancourt's writings. This excellent set of vignettes form the beginnings on a book to be published in the future. The ideas in these writings are central to the course.
- Probability Distribution Explorer. This tool is useful in understanding probability distributions and using them to build models.
Books
- Lambert, A Student's Guide to Bayesian Statistics, Sage Publishing, 2018. This is a very good reference to practitioners of applied Bayesian statistical inference. This book most closely tracks the content of this course.
- Gelman, et al., Bayesian Data Analysis, Third Ed., CRC Press, 2014. "BDA3" is kind of a bible for Bayesian data analysis.
- Richard McElreath, Statistical Rethinking, CRC Press, 2015. This book is a clear, delightful read about Bayesian statistics.
- Blitzstein and Hwang, Introduction to Probability, 2nd Ed., Chapman and Hall/CRC, 2019. This is a beautiful book explaining fundamental concepts in probability and is available for free here.
- Think Bayes by Allen Downey gives an introduction to Bayesian statistical inference.
- D. S. Sivia, Data Analysis: A Bayesian Tutorial, 2nd Ed., Oxford University Press, 2006. Good pedagogical introduction to Bayesian analysis. It is also available in electronic form (though you cannot download the entire book as a PDF) here.
- Phil Gregory, Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press, 2005. This goes into more depth on many of the topics covered in Sivia. It also has an addendum.
- David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. This excellent book beautifiully ties together three related areas of science in a clear, pedagogical, and sometimes humorous manner. (Available for free online from the link.)