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Data-based importance sampling estimates for extreme events
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DOI：10.1016/j.jcp.2020.109429.png)
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Abstract
En 中文
An accurate, efficient, and conceptually simple method is developed to estimate distributions of maxima of solutions of stochastic equations, i.e., ordinary or partial differential equations with random entries. The method is data-based. It constructs importance sampling (IS) or biasing measures from samples of surrogates of full model solutions of stochastic equations and uses these measures and mixtures of surrogate and full model samples to estimate probabilities of extreme events. Numerical examples are presented to illustrate the implementations of the proposed method and demonstrate numerically its performance. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:
Extreme events
Importance sampling measures
Monte Carlo
Nominal measures
Radon-Nikodym theorem
Stochastic equations
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3.8
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1.6W
Citations:
7.4W
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