On the Geometrical Convergence of Gibbs Sampler inRd
AbstractThe 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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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 66 (1998)
Issue (Month): 1 (July)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Yin, G. & Zhang, Q. & Badowski, G., 2000. "Singularly Perturbed Markov Chains: Convergence and Aggregation," Journal of Multivariate Analysis, Elsevier, vol. 72(2), pages 208-229, February.
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