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A backward particle interpretation of Feynman-Kac formulae with applications to filtering and smoothing problems

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If you have a question about this talk, please contact Mustapha Amrani.

Stochastic Partial Differential Equations (SPDEs)

We design a particle interpretation of Feynman-Kac measures on path spaces based on a backward Markovian representation combined with a traditional mean field particle interpretation of the flow of their final time marginals. In contrast to traditional genealogical tree based models, these new particle algorithms can be used to compute normalized additive functionals on-the-fly as well as their limiting occupation measures with a given precision degree that does not depend on the final time horizon. We provide uniform convergence results w.r.t. the time horizon parameter as well as functional central limit theorems and exponential concentration estimates, yielding what seems to be the first results of this type for this class of models. We also illustrate these results in the context of filtering and path estimation problems, as well as in computational physics and imaginary time Schroedinger type partial differential equations, with a special interest in the numerical approximation of the invariant measure associated to h-processes. This is joint work with Arnaud Doucet, and Sumeetpal Singh.

This talk is part of the Isaac Newton Institute Seminar Series series.

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