Geometric Ergodicity of Gibbs Samplers for Bayesian General Linear Mixed Models with Proper Priors
نویسندگان
چکیده
When a Bayesian version of the general linear mixed model is created by adopting a conditionally conjugate prior distribution, a simple block Gibbs sampler can be employed to explore the resulting intractable posterior density. In this article it is shown that, under mild conditions that nearly always hold in practice, the block Gibbs Markov chain is geometrically ergodic.
منابع مشابه
Geometric Ergodicity of Gibbs Samplers
Due to a demand for reliable methods for exploring intractable probability distributions, the popularity of Markov chain Monte Carlo (MCMC) techniques continues to grow. In any MCMC analysis, the convergence rate of the associated Markov chain is of practical and theoretical importance. A geometrically ergodic chain converges to its target distribution at a geometric rate. In this dissertation,...
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