10 research outputs found
A Review on Global Sensitivity Analysis Methods
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