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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

5. A Review on Global Sensitivity Analysis Methods

verfasst von : Bertrand Iooss, Paul Lemaître

Erschienen in: Uncertainty Management in Simulation-Optimization of Complex Systems

Verlag: Springer US

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Abstract

This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smoothing, tests, statistical learning, Monte Carlo, …) aim at determining the model input variables which mostly contribute to an interest quantity depending on model output. This quantity can be for instance the variance of an output variable. Three kinds of methods are distinguished: the screening (coarse sorting of the most influential inputs among a large number), the measures of importance (quantitative sensitivity indices) and the deep exploration of the model behaviour (measuring the effects of inputs on their all variation range). A progressive application methodology is illustrated on a scholar application. A synthesis is given to place every method according to several axes, mainly the cost in number of model evaluations, the model complexity and the nature of brought information.

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Vorheriges Kapitel Uncertainty Management Using Sequential Parameter Optimization
Nächstes Kapitel Connections Among Optimization Models with Uncertainties, ABC and RBV
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Metadaten
Titel
A Review on Global Sensitivity Analysis Methods
verfasst von
Bertrand Iooss
Paul Lemaître
Copyright-Jahr
2015
Verlag
Springer US
Buch
Uncertainty Management in Simulation-Optimization of Complex Systems

Print ISBN: 978-1-4899-7546-1

Electronic ISBN: 978-1-4899-7547-8

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-1-4899-7547-8_5

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