Abstract
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.
Citation
Andrew Leach. Kevin K. Lin. Matthias Morzfeld. "Symmetrized importance samplers for stochastic differential equations." Commun. Appl. Math. Comput. Sci. 13 (2) 215 - 241, 2018. https://doi.org/10.2140/camcos.2018.13.215
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