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Particle filter as a controlled Markov chain for on-line parameter estimation in general state space models

Abstract:
In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE) of the static parameters of a general state space model. Our approach is based on viewing the particle filter as a controlled Markov chain, where the control is the unknown static parameters to be identified, The algorithm relies on the computation of the gradient of the particle filter using a score function approach. © 2006 IEEE.
Publication status:
Published

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Volume:
3
Pages:
329-+
Publication date:
2006-01-01
ISSN:
1520-6149
URN:
uuid:61551d0b-d3b7-46d3-8ffc-00719159aef8
Source identifiers:
172716
Local pid:
pubs:172716
ISBN-10:
142440469X
ISBN-13:
9781424404698

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