高级教程:随机模拟中的输入不确定性和鲁棒分析

H. Lam
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引用次数: 14

摘要

输入不确定性是指由于对随机模拟中用于生成输入变量的概率分布缺乏完全的了解而引起的错误。输入不确定性的量化是仿真界多年来研究的核心问题之一。本教程分两部分概述了一些方法的发展。第一部分讨论了主要的已建立的统计方法,而第二部分从基于鲁棒优化的角度讨论了一些最近的结果,并将它们与已建立的方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation
Input uncertainty refers to errors caused by a lack of complete knowledge about the probability distributions used to generate input variates in stochastic simulation. The quantification of input uncertainty is one of the central topics of interest and has been studied over the years among the simulation community. This tutorial overviews some methodological developments in two parts. The first part discusses major established statistical methods, while the second part discusses some recent results from a robust-optimization-based viewpoint and their comparisons to the established methods.
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