Introduction to the Bayesian approach to statistical inference. Topics include univariate and multivariate models, choice of prior distributions, hierarchical models, computation including Markov chain Monte Carlo, model checking, and model selection.

This site provides some slides  that will be useful for the students:
https://www.ics.uci.edu/~sternh/courses/225/

Students are assumed to have some background in probability and statistical inference (e.g., at the level of the book by Casella and Berger) and some background in statistical methods (e.g., two sample t-tests, linear regression, analysis of variance). ). Also, ideally students will have some experience with statistical computation (e.g., R or Python or MATLAB).

Bayesian Data Analysis by A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. Chapman and Hall/CRC, 2013 (3rd edition).