Introduction to Bayesian Statistics Using Stata Workshop

Introduction to Bayesian Statistics Using Stata Workshop

Presented by Chuck Huber, PhD, Director of Statistical Outreach at StataCorp, Adjunct Associate Professor of Biostatistics, Texas A&M School of Public Health

Bayesian analysis has become a popular tool for many statistical applications. Yet many data analysts have little training in the theory of Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models using Stata. No prior knowledge of Bayesian analysis is necessary and specific topics will include the relationship between likelihood functions, prior, and posterior distributions, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, and how to use Stata's Bayes prefix to fit Bayesian models.

Learning Objectives:

Learn the fundamentals of the Bayesian approach to data analysis.
Learn about prior probability distributions, likelihood functions, posterior probability, and the relationships between them.
Learn about Markov Chain Monte Carlo (MCMC), the Metropolis-Hastings algorithm, Gibbs sampling, and how to diagnose convergence issues with MCMC chains.

Learn how to fit Bayesian regression models and interpret posterior means and credible intervals.

Hosted by the Institute for Clinical and Translational Science  and UCI Center for Statistical Consulting