Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms

Markov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistical computation to explore and estimate features of likelihood surfaces and Bayesian posterior distributions. This paper presents simple conditions which ensure the convergence of two widely used versions of MCMC, the Gibbs sampler and Metropolis-Hastings algorithms.

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Year of publication:	1994
Authors:	Roberts, G. O. ; Smith, A. F. M.
Published in:	Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 49.1994, 2, p. 207-216
Publisher:	Elsevier
Keywords:	Markov chain Monte Carlo Gibbs sampler Metropolis-Hastings algorithm statistical computation ergodicity lower semicontinuity

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008874871