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

Published online by Cambridge University Press:  27 July 2009

I. H. Dinwoodie
I. H. Dinwoodie
Affiliation:
Department of Mathematics, Tulane University, New Orleans, Louisiana 70118
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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
Information
Probability in the Engineering and Informational Sciences , Volume 9 , Issue 2 , April 1995 , pp. 211 - 215
Copyright
Copyright © Cambridge University Press 1995

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References

1.Diaconis, P. & Hanlon, P. (1992). Eigen analysis for the Metropolis algorithm. Contemporary Mathematics 138: 99–117.CrossRefGoogle Scholar
2.Diaconis, P. & Saloff-Coste, L. (1992). Comparison theorems for reversible Markov chains. Annals of Applied Probability 3: 696–730.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: 3–23.Google Scholar