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arXiv · 1707.02695

Symmetrized importance samplers for stochastic differential equations

Andrew Leach·Kevin K. Lin·Matthias Morzfeld
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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.

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Andrew Leach, Kevin K. Lin, Matthias Morzfeld. 2018-03-29. Symmetrized importance samplers for stochastic differential equations. https://doi.org/10.2140/camcos.2018.13.215

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