This week's Statistics Seminar speaker is Jim Hobert from University of Florida.
Talk Title: Convergence Analysis of the Gibbs Sampler for Bayesian General Linear Mixed Models with Improper Priors
A popular default prior for the general linear mixed model is an improper prior that takes a product form with a flat prior on the regression parameter, and so-called power priors on each of the variance components. I will describe a convergence rate analysis of the Gibbs samplers associated with these Bayesian models. The main result is a simple, easily- checked sufficient condition for geometric ergodicity of the Gibbs Markov chain. (This is joint work with Jorge Román and Brett Presnell.)
Refreshments will be served after the seminar in 1181 Comstock Hall.