A common rationale for global sensitivity analysis

E. Borgonovo; G. B. Hazen; E. Plischke

A common rationale for global sensitivity analysis

E. Borgonovo, G. B. Hazen, E. Plischke

Industrial Engineering and Management Sciences

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

Abstract

We show that variance-based, density-based, expected-value of information based sensitivity measures rest on the same rationale, namely, information updating. We prove general results concerning their properties. We then show that they can all be estimated by the same design. The design is suitable in the presence of correlated inputs and makes the estimation cost independent of the number of model inputs and depending solely on the sample size. Results of numerical experiments are proposed.

Original language	English (US)
Pages	3255-3260
Number of pages	6
State	Published - Jan 1 2014
Event	European Safety and Reliability Conference, ESREL 2013 - Amsterdam, Netherlands Duration: Sep 29 2013 → Oct 2 2013

Other

Other	European Safety and Reliability Conference, ESREL 2013
Country	Netherlands
City	Amsterdam
Period	9/29/13 → 10/2/13

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality

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title = "A common rationale for global sensitivity analysis",

abstract = "We show that variance-based, density-based, expected-value of information based sensitivity measures rest on the same rationale, namely, information updating. We prove general results concerning their properties. We then show that they can all be estimated by the same design. The design is suitable in the presence of correlated inputs and makes the estimation cost independent of the number of model inputs and depending solely on the sample size. Results of numerical experiments are proposed.",

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AB - We show that variance-based, density-based, expected-value of information based sensitivity measures rest on the same rationale, namely, information updating. We prove general results concerning their properties. We then show that they can all be estimated by the same design. The design is suitable in the presence of correlated inputs and makes the estimation cost independent of the number of model inputs and depending solely on the sample size. Results of numerical experiments are proposed.

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