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

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. "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

Information

Received: 7 July 2017; Revised: 7 March 2018; Accepted: 25 March 2018; Published: 2018
First available in Project Euclid: 6 July 2018

zbMATH: 06950008
MathSciNet: MR3819577
Digital Object Identifier: 10.2140/camcos.2018.13.215

Subjects:
Primary: 65C05

Rights: Copyright © 2018 Mathematical Sciences Publishers

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Vol.13 • No. 2 • 2018
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