Symmetrized importance samplers for stochastic differential equations
Numerical Analysis
2018-07-04 v2 Computation
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.
Cite
@article{arxiv.1707.02695,
title = {Symmetrized importance samplers for stochastic differential equations},
author = {Andrew Leach and Kevin K. Lin and Matthias Morzfeld},
journal= {arXiv preprint arXiv:1707.02695},
year = {2018}
}
Comments
Added brief discussion of Hamilton-Jacobi equation. Also made various minor corrections. To appear in Communciations in Applied Mathematics and Computational Science