A review on global sensitivity analysis methods

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, \ldots) 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.
Liste complète des métadonnées

Cited literature [89 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00975701
Contributor : Bertrand Iooss <>
Submitted on : Tuesday, April 8, 2014 - 11:00:30 PM
Last modification on : Friday, April 12, 2019 - 4:22:50 PM
Document(s) archivé(s) le : Tuesday, July 8, 2014 - 12:40:11 PM

Files

chapterESF13_iooss.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00975701, version 1
  • ARXIV : 1404.2405

Citation

Bertrand Iooss, Paul Lemaître. A review on global sensitivity analysis methods. C. Meloni and G. Dellino. Uncertainty management in Simulation-Optimization of Complex Systems: Algorithms and Applications, Springer, 2015, ⟨http://www.springer.com/business+%26+management/operations+research/book/978-1-4899-7546-1⟩. ⟨hal-00975701⟩

Share

Metrics

Record views

1480

Files downloads

12733