Bayesian Modeling and Inference
STAT 410 and 420 or equivalent.
The purpose of the course is to provide a blend of theory, methods, and applications. The course will begin with a review of relevant concepts of classical statistical inference, which is needed to compare different paradigms. Following this, elements of the Bayesian inference and decision theory will be introduced in order to emphasize the advantages and challenges of the Bayesian methods. The course will cover a wide range of topics in Bayesian analysis, including objective priors, model selection, Bayesian computations, high-dimensional problems, Bayesian analysis with missing data and finite population sampling. The course emphasizes data analysis via modern computer methods and R freeware packages that are introduced and used throughout the course.