Geometric Ergodicity of Gibbs and Block Gibbs Samplers for a Hierarchical Random Effects Model

James P. Hobert and Charles J. Geyer

Journal of Multivariate Analysis, 1998, vol. 67, issue 2, 414-430

Abstract: We consider fixed scan Gibbs and block Gibbs samplers for a Bayesian hierarchical random effects model with proper conjugate priors. A drift condition given in Meyn and Tweedie (1993, Chapter 15) is used to show that these Markov chains are geometrically ergodic. Showing that a Gibbs sampler is geometrically ergodic is the first step toward establishing central limit theorems, which can be used to approximate the error associated with Monte Carlo estimates of posterior quantities of interest. Thus, our results will be of practical interest to researchers using these Gibbs samplers for Bayesian data analysis.

Keywords: Bayesian model; central limit theorem drift condition Markov chain Monte Carlo rate of convergence variance components (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (9)

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