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Parallel and interacting Markov chain Monte Carlo algorithm

Author

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  • Campillo, Fabien
  • Rakotozafy, Rivo
  • Rossi, Vivien

Abstract

In many situations it is important to be able to propose N independent realizations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov chains (MCMC) interact in order to get an approximation of an independent N-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.

Suggested Citation

  • Campillo, Fabien & Rakotozafy, Rivo & Rossi, Vivien, 2009. "Parallel and interacting Markov chain Monte Carlo algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(12), pages 3424-3433.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:12:p:3424-3433
    DOI: 10.1016/j.matcom.2009.04.010
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/6072 is not listed on IDEAS
    2. Didier Chauveau & Pierre Vandekerkhove, 2002. "Improving Convergence of the Hastings–Metropolis Algorithm with an Adaptive Proposal," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 13-29, March.
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    Cited by:

    1. Fabrizio Leisen & Roberto Casarin & David Luengo & Luca Martino, 2013. "Adaptive Sticky Generalized Metropolis," Working Papers 2013:19, Department of Economics, University of Venice "Ca' Foscari".
    2. Chen, Yuting & Cournède, Paul-Henry, 2014. "Data assimilation to reduce uncertainty of crop model prediction with Convolution Particle Filtering," Ecological Modelling, Elsevier, vol. 290(C), pages 165-177.

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