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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

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
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Funding agency for:
Rainforth, T
Grant:
Industrial Grant
More from this funder
Funding agency for:
Wood, F
Grant:
DARPA PPAML Cooperative Agreement number FA8750-14-2-0006, Sub Award number 61160290-111668
Publisher:
Journal of Machine Learning Research Publisher's website
Host title:
ICML 2016: 33rd International Conference on Machine Learning
Journal:
ICML 2016: 33rd International Conference on Machine Learning Journal website
Publication date:
2016-06-11
Acceptance date:
2016-04-24
ISSN:
1533-7928
Pubs id:
pubs:624354
UUID:
uuid:cf43029b-7133-402a-83f7-314f10aa91d8
Local pid:
pubs:624354
Source identifiers:
624354
Deposit date:
2016-05-27

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