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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, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.ORCID iD: 0000-0003-0772-846X
2019 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 21, p. 5571-5582Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 67, no 21, p. 5571-5582
Keywords [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
National Category
Mathematics
Identifiers
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
Note

QC 20191108

Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2020-03-09Bibliographically approved

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Olsson, Jimmy

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