Module dates/times: Monday, July 20; Tuesday, July 21, and Wednesday, July 22. Live sessions will start no earlier than 8 a.m. Pacific and end no later than 2:30 p.m. Pacific, except for Wednesdays. For modules that end on Wednesday, live sessions will end by 11 a.m. Pacific. For modules that start on Wednesday, live sessions will begin no earlier than 11:30 a.m.
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.