Stochastic simulation for Bayesian inference, designed to develop an understanding of Markov chair Monte Carlo methods and their underlying theoretical framework. Topics include Markov chains, Monte Carlo integration, Gibbs sampler, Metropolis-Hastings algorithms, slice sampling, and sequential Monte Carlo. Three lecture hours a week for one semester. Statistics and Data Sciences 386D and Statistics and Scientific Computation 386D may not both be counted. Prerequisite: Graduate standing; and Economics 392M (Topic 19), Statistics and Data Sciences 384 (or Statistics and Scientific Computation 384), or the equivalent.