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Particle-Based Adaptive-Lag Online Marginal Smoothing in General State-Space Models
Uppsala Univ, Dept Informat Technol, S-75236 Uppsala, Sweden..
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematisk statistik.ORCID-id: 0000-0003-0772-846X
2019 (engelsk)Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, nr 21, s. 5571-5582Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We present a novel algorithm, an adaptive-lag smoother, approximating efficiently, in an online fashion, sequences of expectations under the marginal smoothing distributions in general state-space models. The algorithm evolves recursively a bank of estimators, one for each marginal, in resemblance with the so-called particle-based, rapid incremental smoother (PaRIS). Each estimator is propagated until a stopping criterion, measuring the fluctuations of the estimates, is met. The presented algorithm is furnished with theoretical results describing its asymptotic limit and memory usage.

sted, utgiver, år, opplag, sider
IEEE, 2019. Vol. 67, nr 21, s. 5571-5582
Emneord [en]
Smoothing methods, Approximation algorithms, Markov processes, Signal processing algorithms, Monte Carlo methods, Hidden Markov models, Biological system modeling, Sequential Monte Carlo methods, state-space models, marginal smoothing, PaRIS, particle filters, state estimation
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Identifikatorer
URN: urn:nbn:se:kth:diva-263673DOI: 10.1109/TSP.2019.2941066ISI: 000492374000002Scopus ID: 2-s2.0-85077749795OAI: oai:DiVA.org:kth-263673DiVA, id: diva2:1368788
Merknad

QC 20191108

Tilgjengelig fra: 2019-11-08 Laget: 2019-11-08 Sist oppdatert: 2020-03-09bibliografisk kontrollert

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