ONLINE QUANTIFICATION OF INPUT UNCERTAINTY FOR PARAMETRIC MODELS

被引：0

作者：

Zhou, Enlu ^{[1
]}

Liu, Tianyi ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Ind & Syst Engn, 755 Ferst Dr NW, Atlanta, GA 30332 USA

来源：

2018 WINTER SIMULATION CONFERENCE (WSC) | 2018年

基金：

美国国家科学基金会;

关键词：

SIMULATION EXPERIMENTS; SENSITIVITY;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

It has become increasingly important to assimilate "online data" that arrive sequentially in time for real-time decision. Input uncertainty quantification in stochastic simulation has been developed extensively for batch data that are available all at once, but little has been studied for online data. In this paper, we propose a computationally efficient method to incorporate online data in real time for input uncertainty quantification of parametric models. We show finite-sample bounds and asymptotic convergence for the proposed method, and demonstrate its performance on a simple numerical example.

引用

页码：1587 / 1598

页数：12

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