A review on global sensitivity analysis methods

Abstract. This chapter makes a review, in a complete methodologi-cal framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smooth-ing, tests, statistical learning, Monte Carlo,...) aim at determining the model input variables which mostly contribute to an interest quantity de-pending 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 method-ology 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 in-formation