随机微分方程的对称重要抽样

IF 1.9 3区 数学 Q1 MATHEMATICS, APPLIED
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。
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
Communications in Applied Mathematics and Computational Science MATHEMATICS, APPLIED-PHYSICS, MATHEMATICAL
CiteScore
3.50
自引率
0.00%
发文量
3
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.
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