A review on global sensitivity analysis methods

Bertrand Iooss (1, 2, 3) , Paul Lemaître (3)

1 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques
2 IMT - Institut de Mathématiques de Toulouse UMR5219
3 EDF R&D STEP - Simulation et Traitement de l'information pour l'Exploitation des systèmes de Production

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.

Mots clés: Computer code; Numerical experiment; Uncertainty; Metamodel; Design of experiment

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

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. ⟨hal-00975701⟩