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On variance stabilisation in Population Monte Carlo by double Rao-Blackwellisation

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  • Iacobucci, Alessandra
  • Marin, Jean-Michel
  • Robert, Christian

Abstract

Population Monte Carlo has been introduced as a sequential importance sampling technique to overcome poor fit of the importance function. The performance of the original Population Monte Carlo algorithm is compared with a modified version that eliminates the influence of the transition particle via a double Rao-Blackwellisation. This modification is shown to improve the exploration of the modes through a large simulation experiment on posterior distributions of mean mixtures of distributions.

Suggested Citation

  • Iacobucci, Alessandra & Marin, Jean-Michel & Robert, Christian, 2010. "On variance stabilisation in Population Monte Carlo by double Rao-Blackwellisation," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 698-710, March.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:3:p:698-710
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/6072 is not listed on IDEAS
    2. repec:dau:papers:123456789/6215 is not listed on IDEAS
    3. repec:dau:papers:123456789/1906 is not listed on IDEAS
    4. repec:dau:papers:123456789/6069 is not listed on IDEAS
    5. Celeux, Gilles & Marin, Jean-Michel & Robert, Christian P., 2006. "Iterated importance sampling in missing data problems," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3386-3404, August.
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