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Divide-and-Conquer With Sequential Monte Carlo
Uppsala University, Sweden.
University of Warwick, England.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Intrepid Net Comp, MT USA.
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2017 (English)In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715, Vol. 26, no 2, 445-458 p.Article in journal (Refereed) Published
Abstract [en]

We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conquer approach based upon an auxiliary tree-structured decomposition of the model of interest, turning the overall inferential task into a collection of recursively solved subproblems. The proposed method is applicable to a broad class of probabilistic graphical models, including models with loops. Unlike a standard SMC sampler, the proposed divide-and-conquer SMC employs multiple independent populations of weighted particles, which are resampled, merged, and propagated as the method progresses. We illustrate empirically that this approach can outperform standard methods in terms of the accuracy of the posterior expectation and marginal likelihood approximations. Divide-and-conquer SMC also opens up novel parallel implementation options and the possibility of concentrating the computational effort on the most challenging subproblems. We demonstrate its performance on a Markov random field and on a hierarchical logistic regression problem. Supplementary materials including proofs and additional numerical results are available online.

Place, publisher, year, edition, pages
AMER STATISTICAL ASSOC , 2017. Vol. 26, no 2, 445-458 p.
Keyword [en]
Bayesian methods; Graphical models; Hierarchical models; Particle filters
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-137624DOI: 10.1080/10618600.2016.1237363ISI: 000400182800020OAI: oai:DiVA.org:liu-137624DiVA: diva2:1097342
Note

Funding Agencies|Swedish research Council [637-2014-466]; Probabilistic modeling of dynamical systems [621-2013-5524]; Linnaeus Center CADICS; Engineering and Physical Sciences Research Council [EP/K021672/2]; Natural Sciences and Engineering Research Council (Canada)

Available from: 2017-05-22 Created: 2017-05-22 Last updated: 2017-05-22

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Andersson Naesseth, Christian
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
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