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Resampling from the past to improve on MCMC algorithms

  • Yves Atchade

    ()

    (Department of Mathematics and Statistics, University of Ottawa and LRSP)

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
File Function: First version, 2006
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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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