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Resampling from the past to improve on MCMC algorithms

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Author Info
Yves Atchade () (Department of Mathematics and Statistics, University of Ottawa and LRSP)

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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.

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File URL: http://www.repad.org/ca/on/lrsp/eprop.pdf
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File Function: First version, 2006
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Publisher Info
Paper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number LRSP-WP2.

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Length: 28 pages
Date of creation: 07 Mar 2006
Date of revision:
Handle: RePEc:pqs:wpaper:062006

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Related research
Keywords: Monte Carlo methods Resampling Stochastic volatility models Bayesian phylogeny reconstruction.

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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This page was last updated on 2008-11-17.

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