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Interacting particle Markov chain Monte Carlo

Abstract:

We introduce interacting particle Markov chain Monte Carl (iPMCMC), a PMCMC method that introduces a coupling between multiple 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 total computational budget. An additional advanta...

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Publication status:
Published
Version:
Publisher's version

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Naesseth, CA More by this author
Lindsten, F More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
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Funding agency for:
Rainforth, T
Publisher:
Journal of Machine Learning Research Publisher's website
Publication date:
2016-06-11
ISSN:
1533-7928
URN:
uuid:cf43029b-7133-402a-83f7-314f10aa91d8
Source identifiers:
624354
Local pid:
pubs:624354

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