Introduction to Applied Bayesian Regression Analysis


Instructor: Jun Xu, PhD
Dates: July 28 – August 1, 2024

Course Description

With the exponential growth in computing power, Bayesian analysis 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’ theorem, the basics of likelihood theory, the Markov chain Monte Carlo (MCMC) methods, Bayesian generalized linear regression 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 along with JAGS and Stan.

About the Instructor

Dr. Jun Xu is a full professor of sociology and data science at Ball State University. In addition to his sociological inquiries in Asia and Asian America, demography and health, Dr. Xu’s primary quantitative research/teaching interests include Bayesian statistics, categorical data analysis, causal inference, machine learning, and statistical programming. He is the author of Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan (Chapman & Hall/CRC) and a co-author of Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives (with Andrew S. Fullerton by Chapman and Hall/CRC). His methodological works have appeared in journals such as Sociological Methods & Research, Social Science Research, and The Stata Journal.

In the past two decades or so, Dr. Xu has authored or co-authored several statistical application packages, including gencrm, grcompare, and the very popular SPost (version 9) in Stata and stdcoef in R.

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