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Symmetrized importance samplers for stochastic differential equations. (English) Zbl 1401.65010

Summary: We study a class of importance sampling methods for stochastic differential equations (SDEs). A small noise analysis is performed, and the results suggest that a simple symmetrization procedure can significantly improve the performance of our importance sampling schemes when the noise is not too large. We demonstrate that this is indeed the case for a number of linear and nonlinear examples. Potential applications, e.g., data assimilation, are discussed.

MSC:

65C05 Monte Carlo methods
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References:

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