On the LP-convergence of a Girsanov theorem based particle filter

Abstract : We analyze the L p-convergence of a previously proposed Girsanov theorem based particle filter for discretely observed stochastic differential equation (SDE) models. We prove the convergence of the algorithm with the number of particles tending to infinity by requiring a moment condition and a step-wise initial condition boundedness for the stochastic exponential process giving the likelihood ratio of the SDEs. The practical implications of the condition are illustrated with an Ornstein–Uhlenbeck model and with a non-linear Bene˘ s model.
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Communication dans un congrès
ICASSP 2016 - International Conference on Acoustics, Speech and Signal Processing , Mar 2016, Shangai, China. pp.3989 - 3993, 2016, 〈10.1109/ICASSP.2016.7472426〉
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Contributeur : Eric Moulines <>
Soumis le : lundi 19 décembre 2016 - 20:10:00
Dernière modification le : jeudi 12 avril 2018 - 01:50:20

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Simo Särkkä, Eric Moulines. On the LP-convergence of a Girsanov theorem based particle filter. ICASSP 2016 - International Conference on Acoustics, Speech and Signal Processing , Mar 2016, Shangai, China. pp.3989 - 3993, 2016, 〈10.1109/ICASSP.2016.7472426〉. 〈hal-01419046〉

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