Particle filters for partially observed Markov chains

Abstract : We consider particle filters in a model where the hidden states and the observations form jointly a Markov chain, which means that the hidden states alone do not necessarily form a Markov chain. This model includes as a special case non-linear state-space models with correlated Gaussian noise. Our contribution is to study propagation of errors, stability properties of the filter, and uniform error estimates, using the framework of Le Gland and Oudjane.
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Communication dans un congrès
Proceedings of the IEEE Workshop on Statistical Signal Processing (SSP), Saint―Louis 2003, Sep 2003, Saint―Louis, United States. pp.553-556, 2003, 〈10.1109/SSP.2003.1289524〉
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https://hal.inria.fr/hal-00912045
Contributeur : Francois Le Gland <>
Soumis le : lundi 2 décembre 2013 - 00:42:20
Dernière modification le : mercredi 16 mai 2018 - 11:23:05

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Natacha Caylus, Arnaud Guyader, François Le Gland. Particle filters for partially observed Markov chains. Proceedings of the IEEE Workshop on Statistical Signal Processing (SSP), Saint―Louis 2003, Sep 2003, Saint―Louis, United States. pp.553-556, 2003, 〈10.1109/SSP.2003.1289524〉. 〈hal-00912045〉

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