Markdown scripts for webinar series "Bayesian Thinking: Fundamentals, Regression and Multilevel Modeling" Jim Albert and Jingchen (Monika) Hu
This series of webinars 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: Introduction to Bayesian Inference / Bayesian Regression. 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. Implementation of Bayesian thinking for regression models for continuous response data.
Part 2: Bayesian Regression / Multimodel Modeling. Implementation of Bayesian thinking for regression models for categorical response data. Introduction to multilevel models as a flexible way of modeling regressions over groups.
Instructors: Jim Albert is Emeritus Professor at Bowling Green State University and Jingchen (Monika) Hu is Assistant Professor at Vassar College. They are coauthors of the text Probability and Bayesian Modeling published by Chapman and Hall in 2019.