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High-Dimensional Filtering Using Nested Sequential Monte Carlo
Linkoping Univ, Div Stat & Machine Learning, S-58183 Linkoping, Sweden.
Linkoping Univ, Div Stat & Machine Learning, S-58183 Linkoping, Sweden.ORCID iD: 0000-0003-3749-5820
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
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
Signal Processing Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-391279DOI: 10.1109/TSP.2019.2926035ISI: 000476798500004OAI: oai:DiVA.org:uu-391279DiVA, id: diva2:1345012
Funder
Swedish Research Council, 2016-04278Swedish Research Council, 621-2016-06079Swedish Foundation for Strategic Research , RIT15-0012Swedish Foundation for Strategic Research , ICA16-0015Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved

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Schön, Thomas B.

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