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Monte Carlo methods in nonlinear filtering and importance sampling

François Le Gland 1 
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : For the calculation of conditional expectations in nonlinear filtering of Markov processes, one may think to use Monte Carlo techniques, as an alternative to the numerical solution of Zakai equation (a stochastic PDE). We show that a direct implementation of this idea is unefficient, and we propose a modified algorithm, that uses importance sampling, where our choice of the new probability is based on large deviations arguments.
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Submitted on : Sunday, December 1, 2013 - 11:30:54 PM
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François Le Gland. Monte Carlo methods in nonlinear filtering and importance sampling. Proceedings of the 23rd IEEE Conference on Decision and Control, Las Vegas 1984, IEEE--CSS, Dec 1984, Las Vegas, United States. pp.31-32, ⟨10.1109/CDC.1984.272246⟩. ⟨hal-00912071⟩



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