On the Geometrical Convergence of Gibbs Sampler inRd

The geometrical convergence of the Gibbs sampler for simulating a probability distribution inRdis proved. The distribution has a density which is a bounded perturbation of a log-concave function and satisfies some growth conditions. The analysis is based on a representation of the Gibbs sampler and some powerful results from the theory of Harris recurrent Markov chains.

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Year of publication:	1998
Authors:	Hwang, Chii-Ruey ; Sheu, Shuenn-Jyi
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 66.1998, 1, p. 22-37
Publisher:	Elsevier
Subject:	Stochastic relaxation \| Gibbs sampler \| Markov chain \| geometrical convergence \| Harris recurrence \| Monte Carlo Markov chain \| Metropolis algorithm \| nonlinear autoregression

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

Persistent link: https://www.econbiz.de/10005093862