Bayesian Thinking: Fundamentals, Regression, and Multilevel Modeling

Course description

This series of webinar provides a general introduction to Bayesian modeling with a particular focus on regression and multilevel models. The use of the system R in Bayesian computation is described, including the programming of the Bayesian model and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo (MCMC) algorithms and diagnostic methods to assess convergence of the algorithms. The LearnBayes and rethinking R packages are used to illustrate MCMC fitting by the use of Gibbs sampling and Metropolis algorithms. Larger Bayesian models will be fit using JAGS and Stan and the accompanying runjags and rstan packages.

Part 1 (2 hours): Introduction to Bayesian Inference. Basic tenets of Bayesian thinking including construction of priors, summarization of the posterior to perform inferences, and the use of prediction distributions for prediction and model checking.

Part 2 (2 hours): Bayesian Regression. Implementation of Bayesian thinking for regression models for continuous and categorical response data.

Part 3 (2 hours): Bayesian Multilevel Modeling. Introduction to multilevel models as a flexible way of modeling regressions over groups.