Paper Info
Title
Data-based importance sampling estimates for extreme events
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.jcp.2020.109429
Journal of Computational Physics
Keywords
DocType
Volume
Extreme events,Importance sampling measures,Monte Carlo,Nominal measures,Radon-Nikodym theorem,Stochastic equations
Journal
412
ISSN
Citations 
PageRank 
0021-9991
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Mircea Grigoriu143.83