Online sequential Monte Carlo smoother for partially observed diffusion processes

Abstract : is paper introduces a new algorithm to approximate smoothed additive functionals of partially observed diffusion processes. This method relies on a new sequential Monte Carlo method which allows to compute such approximations online, i.e., as the observations are received, and with a computational complexity growing linearly with the number of Monte Carlo samples. The original algorithm cannot be used in the case of partially observed stochastic differential equations since the transition density of the latent data is usually unknown. We prove that it may be extended to partially observed continuous processes by replacing this unknown quantity by an unbiased estimator obtained for instance using general Poisson estimators. This estimator is proved to be consistent and its performance are illustrated using data from two models.
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Article dans une revue
EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2018, 2018 (1), 〈10.1186/s13634-018-0530-3〉
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https://hal-agrocampus-ouest.archives-ouvertes.fr/hal-01962151
Contributeur : Catherine Cliquet <>
Soumis le : jeudi 20 décembre 2018 - 14:50:53
Dernière modification le : jeudi 28 février 2019 - 17:45:24
Document(s) archivé(s) le : vendredi 22 mars 2019 - 13:14:38

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Pierre Gloaguen, Marie-Pierre Etienne, Sylvain Le Corff. Online sequential Monte Carlo smoother for partially observed diffusion processes. EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2018, 2018 (1), 〈10.1186/s13634-018-0530-3〉. 〈hal-01962151〉

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