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Scaling analysis of multiple-try MCMC methods


  • Bédard, Mylène
  • Douc, Randal
  • Moulines, Eric


Multiple-try methods are extensions of the Metropolis algorithm in which the next state of the Markov chain is selected among a pool of proposals. These techniques have witnessed a recent surge of interest because they lend themselves easily to parallel implementations. We consider extended versions of these methods in which some dependence structure is introduced in the proposal set, extending earlier work by Craiu and Lemieux (2007).

Suggested Citation

  • Bédard, Mylène & Douc, Randal & Moulines, Eric, 2012. "Scaling analysis of multiple-try MCMC methods," Stochastic Processes and their Applications, Elsevier, vol. 122(3), pages 758-786.
  • Handle: RePEc:eee:spapps:v:122:y:2012:i:3:p:758-786
    DOI: 10.1016/

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    Cited by:

    1. L. Martino & F. Louzada, 2017. "Issues in the Multiple Try Metropolis mixing," Computational Statistics, Springer, vol. 32(1), pages 239-252, March.
    2. repec:eee:spapps:v:127:y:2017:i:12:p:4053-4082 is not listed on IDEAS


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