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High-Dimensional Filtering Using Nested Sequential Monte Carlo
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
Uppsala Univ, Sweden.
2019 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 16, p. 4177-4188Article in journal (Refereed) Published
Abstract [en]

Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions. We propose nested sequential Monte Carlo, a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correctSMCalgorithm. This way, we can compute an "exact approximation" of, e. g., the locally optimal proposal, and extend the class of models forwhichwe can perform efficient inference using SMC. We showimproved accuracy over other state-of-the-art methods on several spatio-temporal state-space models.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 67, no 16, p. 4177-4188
Keywords [en]
Particle filtering; spatio-temporal models; state space models; approximate Bayesian inference; backward simulation
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159547DOI: 10.1109/TSP.2019.2926035ISI: 000476798500004OAI: oai:DiVA.org:liu-159547DiVA, id: diva2:1342490
Note

Funding Agencies|Swedish Research Council [2016-04278, 621-2016-06079]; Swedish Foundation for Strategic Research [RIT15-0012, ICA16-0015]

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • 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
  • asciidoc
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