Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D., Vehtari, A., and Rubin, D. B. (2013) *Bayesian Data Analysis*, Third Edition, Chapman & Hall/CRC.

This course gives students a rigorous introduction to the theory of Bayesian Statistical Inference and Data Analysis, including prior and posterior distributions, Bayesian estimation and testing, Bayesian computation theories and methods, and implementation of Bayesian computation methods using popular statistical software. The early part of the course focuses on fundamental Bayesian inference and data analysis. The second part covers more advanced topics including various sampling methods and regression models. Compared to the existing course on Bayesian statistics, including STAT 610 and BIOSTATS 730, this course covers more comprehensive topics on Bayesian statistical models and computation. It also emphasizes more on theoretical basis of various Bayesian models and some advanced computing methods. It prepares students for research in statistics and related areas as well as application of Bayesian data analysis for complex real-world problems.

## Department of Mathematics and Statistics