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Symmetrized
importance samplers for stochastic differential equations
Andrew Leach, Kevin K. Lin and Matthias Morzfeld |
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Vol. 13 (2018), No. 2, 215–241
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Abstract |
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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. |
Keywords
importance sampling, stochastic differential equations,
small noise theory, symmetrization, data assimilation
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Mathematical Subject Classification 2010
Primary: 65C05
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Milestones
Received: 7 July 2017
Revised: 7 March 2018
Accepted: 25 March 2018
Published: 5 June 2018
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Authors |
| Andrew Leach | |
| Program in Applied Mathematics University of Arizona Tucson, AZ United States |
Google Mountain View, CA United States |
| Kevin K. Lin | |
| Program in Applied Mathematics University of Arizona Tucson, AZ United States |
Department of Mathematics University of Arizona Tucson, AZ United States |
| Matthias Morzfeld | |
| Program in Applied Mathematics University of Arizona Tucson, AZ United States |
Department of Mathematics University of Arizona Tucson, AZ United States |
