Interacting Particle Markov Chain Monte Carlo

Arnaud Doucet; Brooks Paige; Christian A. Naesseth; Frank Wood; Fredrik Lindsten; Jan-Willem van de Meent; Tom Rainforth

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Interacting Particle Markov Chain Monte Carlo

Tom Rainforth , Christian A. Naesseth , Fredrik Lindsten , Brooks Paige , Jan-Willem van de Meent , Arnaud Doucet , Frank Wood This is my paper

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We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both non-interacting PMCMC samplers, and a single PMCMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.

Interacting Particle Markov Chain Monte Carlo

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