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Pré-Publication, Document De Travail Année : 2006

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

Résumé

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 involved functionals will be of additive form for a large subclass of models. To cope with the problem in this case, a modification, relying on forgetting properties of the filtering dynamics, of the standard method is proposed. In this setting, the quality of the produced estimates is investigated both theoretically and through simulations.
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Dates et versions

hal-00096080 , version 1 (18-09-2006)
hal-00096080 , version 2 (06-03-2008)

Identifiants

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Jimmy Olsson, Olivier Cappé, Randal Douc, Eric Moulines. Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models. 2006. ⟨hal-00096080v1⟩
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