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Interacting Particle Markov Chain Monte Carlo
University of Oxford.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. christian.a.naesseth@liu.se.
Uppsala University.
University of Oxford.
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2016 (English)In: Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Sequential Monte Carlo, Probabilistic programming, parallelisation
National Category
Computer Science Control Engineering Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-130043OAI: oai:DiVA.org:liu-130043DiVA: diva2:946692
Conference
International Conference on Machine Learning (ICML), New York, USA, June 19-24, 2016
Projects
CADICS
Funder
Cancer and Allergy Foundation
Available from: 2016-07-05 Created: 2016-07-05 Last updated: 2016-07-11

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http://jmlr.org/proceedings/papers/v48/rainforth16.html

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
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  • nn-NB
  • sv-SE
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Output format
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