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arXiv · 1602.05128

Interacting Particle Markov Chain Monte Carlo

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

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.

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Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank Wood. 2016-02-16. Interacting Particle Markov Chain Monte Carlo. https://arxiv.org/abs/1602.05128

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