This course introduces theoretical foundations and practical methods of Bayesian approaches to statistical inference in a variety of applications. Topics include modern statistical decision theory, prior distributions, Bayes' rule for posterior probabililties, Bayesian inference for means and proportions, simple and hierarchical Bayesian models with model diagnostics, selection and computation (Markov Chain Monte Carlo, Metropolis-Hastings algorithm, and Gibbs Sampling). The course uses statistical programming tools for computational and empirical studies. Prerequisite: STAT 704 or consent of instructor. (F;S;SS).