This course extends the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems. The course is designed for analysts with some familiarity with Bayesian analysis who want to deepen their skill set in Bayesian modeling. Students will learn how to define three-stage hierarchical models, how to implement them using Winbugs in multilevel, meta-analytic and regression applications, and how to assess goodness of fit. Continuous, count and binary outcomes are also covered.
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.