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Interacting Particle Markov Chain Monte Carlo
The University of Oxford, Oxford, United Kingdom.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Uppsala University, Uppsala, Sweden.
The University of Oxford, Oxford, United Kingdom.
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2016 (English)In: Proceedings of the 33rd International Conference on Machine Learning, 2016, Vol. 48Conference 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 noninteracting 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. Vol. 48
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159493OAI: oai:DiVA.org:liu-159493DiVA, id: diva2:1341647
Conference
33rd International Conference on Machine Learning, New York, NY, USA, June 19 - 24, 2016
Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-08-16Bibliographically approved

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Andersson Naesseth, Christian

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