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EFFICIENT PARTICLE-...
EFFICIENT PARTICLE-BASED ONLINE SMOOTHING IN GENERAL HIDDEN MARKOV MODELS
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- Westerborn, Johan (författare)
- KTH,Matematik (Inst.),KTH, Matematik (Inst.), Sweden
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- Olsson, Jimmy (författare)
- KTH,Matematik (Inst.),KTH, Matematik (Inst.), Sweden
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KTH Matematik (Inst) (creator_code:org_t)
- 2014
- 2014
- Engelska.
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Ingår i: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. - 1520-6149.
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
Nyckelord
- Hidden Markov models
- particle filters
- smoothing methods
- Monte Carlo methods
- state estimation
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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