An introduction to the Bayesian approach to statistical inference. Topics include: basic probability theory, Bayes Theorem, prior distributions and conjugate priors, Markov Chain Monte Carlo methods, applications of Bayesian statistics (e.g. Bayesian networks, decision trees). Prerequisite: STAT 204 or MATH 321.