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Resampling from the past to improve on MCMC algorithms

Author

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  • Yves Atchade

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

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.

Suggested Citation

  • Yves Atchade, 2006. "Resampling from the past to improve on MCMC algorithms," RePAd Working Paper Series LRSP-WP2, Département des sciences administratives, UQO.
    • Handle: RePEc:pqs:wpaper:062006
    as

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    File URL: http://www.repad.org/ca/on/lrsp/eprop.pdf
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    References listed on IDEAS

    as
    1. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Monte Carlo methods; Resampling; Stochastic volatility models; Bayesian phylogeny reconstruction.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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