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EFFICIENT PARTICLE-BASED ONLINE SMOOTHING IN GENERAL HIDDEN MARKOV MODELS
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).ORCID iD: 0000-0001-9565-7686
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).ORCID iD: 0000-0003-0772-846X
2014 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ISSN 1520-6149Article in journal (Refereed) Published
Abstract [en]

This paper deals with the problem of estimating expectations of sums of additive functionals under the joint smoothing distribution in general hidden Markov models. Computing such expectations is a key ingredient in any kind of expectation-maximization-based parameter inference in models of this sort. The paper presents a computationally efficient algorithm for online estimation of these expectations in a forward manner. The proposed algorithm has a linear computational complexity in the number of particles and does not require old particles and weights to be stored during the computations. The algorithm avoids completely the well-known particle path degeneracy problem of the standard forward smoother. This makes it highly applicable within the framework of online expectation-maximization methods. The simulations show that the proposed algorithm provides the same precision as existing algorithms at a considerably lower computational cost.

Place, publisher, year, edition, pages
2014.
Keyword [en]
Hidden Markov models, particle filters, smoothing methods, Monte Carlo methods, state estimation
National Category
Fluid Mechanics and Acoustics
Identifiers
URN: urn:nbn:se:kth:diva-158346DOI: 10.1109/ICASSP.2014.6855159ISI: 000343655308009Scopus ID: 2-s2.0-84905270424OAI: oai:DiVA.org:kth-158346DiVA: diva2:782536
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 04-09, 2014, Florence, ITALY
Note

QC 20150121

Available from: 2015-01-21 Created: 2015-01-07 Last updated: 2017-12-05Bibliographically approved

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Westerborn, JohanOlsson, Jimmy

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CiteExportLink to record
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  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • nn-NB
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  • Other locale
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Output format
  • html
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  • asciidoc
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