Title
An Efficient Approach to Deal with the Curse of Dimensionality in Sensitivity Analysis Computations
Abstract
This paper deals with computations of sensitivity indices in global sensitivity analysis. Given a model y=f(x1,...,xk), where the k input factors xi's are uncorrelated with one another, one can see y as the realisation of a stochastic process obtained by sampling each of the xi's from its marginal distribution. The sensitivity indices are related to the decomposition of the variance of y into terms either due to each xi taken singularly, as well as into terms due to the cooperative effects of more than one. When the complete decomposition is considered, the number of sensitivity indices to compute is (2k-1), making the computational cost grow exponentially with k. This has been referred to as the curse of dimensionality and makes the complete decomposition unfeasible in most practical applications. In this paper we show that the information contained in the samples used to compute suitably defined subsets A of the (2k-1) indices can be used to compute the complementary subsets A* of indices, at no additional cost. This property allows reducing significantly the growth of the computational costs as k increases.
Year
DOI
Venue
2002
10.1007/3-540-46043-8_19
International Conference on Computational Science (1)
Keywords
Field
DocType
efficient approach,sensitivity index,k input factors xi,k increase,additional cost,complete decomposition,complementary subsets,global sensitivity analysis,computational cost,complete decomposition unfeasible,sensitivity analysis computations,model y,stochastic process,sensitivity analysis,curse of dimensionality
Mathematical optimization,Stochastic process,Curse of dimensionality,Realisation,Sampling (statistics),Global sensitivity analysis,Marginal distribution,Mathematics,Computation,Exponential growth
Conference
Volume
ISSN
ISBN
2329
0302-9743
3-540-43591-3
Citations 
PageRank 
References 
0
0.34
1
Authors
2
Name
Order
Citations
PageRank
Marco Ratto128721.99
Andrea Saltelli255451.20