Resampling from the past to improve on MCMC algorithms

Yves Atchade

No LRSP-WP2, RePAd Working Paper Series from Département des sciences administratives, UQO

Abstract: We 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.

Keywords: Monte Carlo methods; Resampling; Stochastic volatility models; Bayesian phylogeny reconstruction. (search for similar items in EconPapers)
JEL-codes: C10 C40 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2006-03-07
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:pqs:wpaper:062006

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