Resampling from the past to improve on MCMC algorithms

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dc.creatorAtchade, Yves F.
dc.date2006-05-16
dc.date.accessioned2026-07-07T08:07:49Z
dc.date.available2026-07-07T08:07:49Z
dc.descriptionWe introduce the idea that resampling from past observations in a Markov Chain Monte Carlo sampler can fasten convergence. We prove that proper resampling from the past does not disturb the limit distribution of the algorithm. We illustrate the method with two examples. The first on a Bayesian analysis of stochastic volatility models and the other on Bayesian phylogeny reconstruction.
dc.description26 pages, 7 figures
dc.identifierhttps://arxiv.org/abs/math/0605452
dc.identifierhttp://arxiv.org/abs/math/0605452
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131056
dc.subjectStatistics Theory
dc.subjectProbability
dc.subject60C05; 60J27; 60J35; 65C40
dc.titleResampling from the past to improve on MCMC algorithms
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