Contact nelsod6@uw.edu for updated space availability.

This module assumes knowledge of the material in Module 1: Probability and Statistical Inference and working knowledge of the R programming language.

This module is an introduction to Markov chain Monte Carlo (MCMC) methods. The first half of 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 progress from generic toy problems to infectious disease applications, which include chain-binomial and general epidemic models. Programming will be in R. Individuals already familiar with MCMC methods and knowledge of R programming should consider MCMC II.