A framework for input uncertainty analysis
A framework for input uncertainty analysis
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输入不确定性分析框架
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Wei Xie
中科院分区：
文献类型：
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作者：
R. Barton;B. Nelson;Wei Xie
我们考虑的问题产生的置信区间的平均响应的系统表示的随机模拟，是由输入模型，估计从“真实世界”的数据。因此，我们希望置信区间既考虑输入模型的不确定性，又考虑模拟输出中的随机噪声;标准实践仅考虑随机噪声。为了实现这一目标，我们引入元模型辅助自举，并说明其性能相对于其他建议处理输入的不确定性两个示例。
We consider the problem of producing confidence intervals for the mean response of a system represented by a stochastic simulation that is driven by input models that have been estimated from “real-world” data. Therefore, we want the confidence interval to account for both uncertainty about the input models and stochastic noise in the simulation output; standard practice only accounts for the stochastic noise. To achieve this goal we introduce metamodel-assisted bootstrapping, and illustrate its performance relative to other proposals for dealing with input uncertainty on two queueing examples.