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Backward sequential Monte Carlo for marginal smoothing
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2014 (English)In: Proc. 18th Workshop on Statistical Signal Processing, Piscataway, NJ: IEEE , 2014, 368-371 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose a new type of particle smoother with linear computational complexity. The smoother is based on running a sequential Monte Carlo sampler backward in time after an initial forward filtering pass. While this introduces dependencies among the backward trajectories we show through simulation studies that the new smoother can outperform existing forward-backward particle smoothers when targeting the marginal smoothing densities.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE , 2014. 368-371 p.
Keyword [en]
Sequential Monte Carlo, Particle filter, Particle smoother, Forward-backward algorithms
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-265973DOI: 10.1109/SSP.2014.6884652ISI: 000361019700093ISBN: 978-1-4799-4975-5 (print)OAI: oai:DiVA.org:uu-265973DiVA: diva2:867143
Conference
SSP 2014, June 29–July 2, Gold Coast, Australia
Available from: 2015-11-04 Created: 2015-11-04 Last updated: 2015-11-04Bibliographically approved

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Schön, Thomas B.

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