Prerequisites: Students are expected to have a working knowledge of the R computing environment. Programming will be in R. Students new to R should complete a tutorial before the module. This module assumes knowledge of the material in Module 1: Probability and Statistical Inference, though not necessarily from taking that module.

This module is an introduction to Markov chain Monte Carlo (MCMC) methods. The course includes a general introduction to Bayesian statistics, Monte Carlo, and MCMC. Some relevant facts from the Markov chain theory are reviewed. Algorithms include Gibbs sampling and Metropolis-Hastings. A practical introduction to convergence diagnostics is included. Motivating practical examples range from generic toy problems to infectious disease applications, which include chain-binomial and general epidemic models. A hierarchical model will be covered. The module will alternate between lectures and computer labs. Individuals already familiar with MCMC methods and knowledge of R programming should consider MCMC II.