Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-28T18:14:25.328Z Has data issue: false hasContentIssue false

A Bound on the Rate of Convergence for the Discrete Gibbs Sampler

Published online by Cambridge University Press:  27 July 2009

I. H. Dinwoodie
Affiliation:
Department of Mathematics, Tulane University, New Orleans, Louisiana 70118

Abstract

We give a computable bound on the rate of convergence of the occupation measure for the Gibbs sampler to the stationary distribution.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Diaconis, P. & Hanlon, P. (1992). Eigen analysis for the Metropolis algorithm. Contemporary Mathematics 138: 99117.CrossRefGoogle Scholar
2.Diaconis, P. & Saloff-Coste, L. (1992). Comparison theorems for reversible Markov chains. Annals of Applied Probability 3: 696730.Google Scholar
3.Dinwoodie, I.H. (1995). A probability inequality for the occupation measure of a reversible Markov chain. Annals of Applied Probability (to appear).CrossRefGoogle Scholar
4.Smith, A.F.M. & Roberts, G.O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B 55: 323.Google Scholar