Data Assimilation Meetings at Reading

Date Meeting type Speakers
5 June 2013 Invited speaker Dan Crisan (Imperial College)
Generalized Particle Filters. (pdf)
Stochastic filtering is defined as the estimation of dynamical systems whose trajectory is modelled by a stochastic process called the signal, given the information accumulated from its partial observation. A massive scientific and computational effort is dedicated to the development of various tools for approximating the solution of the filtering problem. The idea of using sums of Gaussian measures to approximate the solution of the filtering problem was first introduced over a decade ago and has since been used by both practitioners and academics. However, most of the existing work on this area is based on the success of the numerical implementations, and little or no rigorous convergence analysis of such approximations has been done. This paper fills this gap and contains a rigorous analysis of the approximation of the solution of the filtering problem using mixtures of Gaussian measures. In particular, I present the L2-convergence rate for the approximating system and the corresponding central limit theorem.

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