Past Class


Introduction to Bayesian Analysis Using STATA

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.

Course Slides

INSTRUCTOR: Chuck Huber, Adjunct Associate Professor of Biostatistics, Texas A&M School of Public Health (and Senior Statistician from StataCorp