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Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models

Authors
  • Olsson, Jimmy
  • Cappé, Olivier
  • Douc, Randal
  • Moulines, Eric
Type
Published Article
Publication Date
Mar 06, 2008
Submission Date
Sep 19, 2006
Identifiers
DOI: 10.3150/07-BEJ6150
arXiv ID: math/0609514
Source
arXiv
License
Unknown
External links

Abstract

This paper concerns the use of sequential Monte Carlo methods (SMC) for smoothing in general state space models. A well-known problem when applying the standard SMC technique in the smoothing mode is that the resampling mechanism introduces degeneracy of the approximation in the path space. However, when performing maximum likelihood estimation via the EM algorithm, all functionals involved are of additive form for a large subclass of models. To cope with the problem in this case, a modification of the standard method (based on a technique proposed by Kitagawa and Sato) is suggested. Our algorithm relies on forgetting properties of the filtering dynamics and the quality of the estimates produced is investigated, both theoretically and via simulations.

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