With the exponential growth in computing power, Bayesian statistics has metamorphosed from an obscure alternative to a serious challenger of the traditional frequentist statistics. This workshop introduces the conceptual background, computational procedures, and statistical techniques for doing Bayesian regression analysis, with a focus on application and interpretation. Selected topics covered include the historical background of the Bayesian framework in comparison with the classical frequentist approach, the Bayes’s theorem, the basics of likelihood theory, the Markov chain Monte Carlo (MCMC) methods, Bayesian GLM and advanced models, and post-estimation analyses. The first component of each lecture covers important concepts and techniques, and the second component teaches the workflow of doing Bayesian data analysis using R and Stan.
Dr. Jun Xu is professor of sociology and data science at Ball State University. His quantitative research/teaching interests include Bayesian statistics, categorical data analysis, causal inference, machine learning, and statistical programming. His methodological works have appeared in journals such as Sociological Methods and Research, Social Science Research, and The Stata Journal. He is an author of Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives (with Dr. Andrew S. Fullerton by Chapman & Hall), and he is the author of another statistical monograph on modern regression analysis forthcoming with the same publisher. In the past two decades or so, he has authored or co-authored several statistical application packages, including gencrm, grcompare, and the popular SPost9.0 package in Stata and stdcoef in R.