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A new framework of variance based global sensitivity analysis for models with correlated inputs

机译:具有相关输入的模型的基于方差的全局灵敏度分析的新框架

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In the past few decades, variance based global sensitivity analysis for models with only uncorrelated inputs has been well developed. It aims at investigating the impact of variations in uncorrelated inputs on the variation of a model output and ranking the importance of the inputs. However, for models with correlated inputs, only a few researches have been done and the existing theory of variance based global sensitivity is not so consummate. In this article, a new framework of variance based global sensitivity analysis is presented, which is suitable for models with both uncorrelated and correlated inputs. With this new framework, the variance based global sensitivity analysis for models with correlated variables can be conducted conveniently and the variance contributions of a correlated variable to the variance of model output can be identified and interpreted distinctly. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在过去的几十年中,对于仅具有不相关输入的模型,基于方差的全局灵敏度分析已经得到了很好的发展。它旨在调查不相关输入的变化对模型输出的变化的影响,并对输入的重要性进行排名。然而,对于具有相关输入的模型,仅进行了很少的研究,并且基于方差的全局敏感性的现有理论还不那么完善。在本文中,提出了一种基于方差的全局敏感性分析的新框架,该框架适用于具有不相关和相关输入的模型。使用这个新框架,可以方便地对具有相关变量的模型进行基于方差的全局敏感性分析,并且可以清楚地识别和解释相关变量对模型输出方差的方差贡献。 (C)2015 Elsevier Ltd.保留所有权利。

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