In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan. Topics covered include Gibbs sampling and the Metropolis-Hastings method. Participants also learn how to implement linear regression (normal and t errors), Poisson regression, binary/binomial regression, and ordinal regression.
Dr. Peter Congdon
Dr. Peter Congdon is a Research Professor in Quantitative Geography and Health Statistics at Queen Mary University of London. He is the author of several books and numerous articles in peer-reviewed journals. His research interests include spatial data analysis, Bayesian statistics, latent variable models, and epidemiology.