Skip to Main content Skip to Navigation
Other publications

Global Sensitivity Analysis of Stochastic Computer Models with joint metamodels

Abstract : The global sensitivity analysis method, used to quantify the influence of uncertain input variables on the response variability of a numerical model, is applicable to deterministic computer code (for which the same set of input variables gives always the same output value). This paper proposes a global sensitivity analysis methodology for stochastic computer code (having a variability induced by some uncontrollable variables). The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, non parametric joint models (based on Generalized Additive Models and Gaussian processes) are discussed. The relevance of these new models is analyzed in terms of the obtained variance-based sensitivity indices with two case studies. Results show that the joint modeling approach leads accurate sensitivity index estimations even when clear heteroscedasticity is present.
Document type :
Other publications
Complete list of metadatas

Cited literature [39 references]  Display  Hide  Download
Contributor : Bertrand Iooss <>
Submitted on : Monday, June 8, 2009 - 11:34:57 AM
Last modification on : Monday, May 18, 2020 - 2:36:27 PM
Long-term archiving on: : Thursday, September 23, 2010 - 5:53:55 PM


Files produced by the author(s)


  • HAL Id : hal-00232805, version 3
  • ARXIV : 0802.0443



Bertrand Iooss, Mathieu Ribatet, Amandine Marrel. Global Sensitivity Analysis of Stochastic Computer Models with joint metamodels. 2009. ⟨hal-00232805v3⟩



Record views


Files downloads