Symmetrized importance samplers for stochastic differential equations
For researchers using importance sampling in SDEs (e.g., data assimilation), this offers a simple improvement, though the analysis is limited to small-noise regimes.
The paper proposes symmetrized importance samplers for SDEs, showing via small-noise analysis and examples that symmetrization improves performance for moderate noise levels.
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.