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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.
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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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