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Backward sequential Monte Carlo for marginal smoothing
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. (C-Research)
Uppsala University.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9424-1272
2014 (English)In: Proceedings of the 2014 IEEE Statistical Signal Processing Workshop, IEEE Press, 2014, 368-371 p.Conference 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
IEEE Press, 2014. 368-371 p.
National Category
Signal Processing
URN: urn:nbn:se:liu:diva-107138DOI: 10.1109/SSP.2014.6884652ISI: 000361019700093OAI: diva2:722012
2014 IEEE Statistical Signal Processing Workshop (SSP), 29 June - 02 July 2014, Gold Coast, Australia
Available from: 2014-06-05 Created: 2014-06-05 Last updated: 2016-05-04

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Kronander, JoelDahlin, Johan
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