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Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-0355-2663
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1927-1690
2017 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 12, p. 1813-1817Article in journal (Refereed) Published
Abstract [en]

We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. We propose a two-step algorithm: a method of moments estimator (formulated as a convex optimization problem) followed by a single iteration of a Newton-Raphson maximum-likelihood estimator. The two-fold contribution of this letter is, first, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and second, to numerically show that the method is computationally less demanding than conventional methods-in particular for large datasets.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 24, no 12, p. 1813-1817
Keywords [en]
Hidden Markov models (HMM), maximum-likelihood (ML), method of moments, system identification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-217930DOI: 10.1109/LSP.2017.2759902ISI: 000413962800006Scopus ID: 2-s2.0-85031786373OAI: oai:DiVA.org:kth-217930DiVA, id: diva2:1158879
Note

QC 20171121

Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2017-11-21Bibliographically approved

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Mattila, RobertRojas, Cristian R.Wahlberg, Bo

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