随机微分方程的对称重要抽样

IF 1.9 3区数学 Q1 MATHEMATICS, APPLIED

Communications in Applied Mathematics and Computational Science Pub Date : 2017-07-10 DOI:10.2140/camcos.2018.13.215

Andrew B. Leach, Kevin K. Lin, M. Morzfeld

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引用次数: 2

摘要

研究了一类随机微分方程的重要抽样方法。进行了小噪声分析，结果表明，当噪声不太大时，简单的对称处理可以显著提高我们的重要采样方案的性能。我们证明，对于一些线性和非线性的例子，确实是这样。讨论了潜在的应用，例如数据同化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Symmetrized importance samplers for stochastic differential equations

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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来源期刊

Communications in Applied Mathematics and Computational Science MATHEMATICS, APPLIED-PHYSICS, MATHEMATICAL

CiteScore

3.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： CAMCoS accepts innovative papers in all areas where mathematics and applications interact. In particular, the journal welcomes papers where an idea is followed from beginning to end — from an abstract beginning to a piece of software, or from a computational observation to a mathematical theory.