Shao Q. auteur aut IRD Younes Anis auteur aut IRD Fahs M. auteur aut IRD Mara T. A. auteur aut IRD text journalArticle eng text/pdf born digital access Polynomial chaos expansions are frequently used by engineers and modellers for uncertainty and sensitivity analyses of computer models. They allow representing the input/output relations of computer models. Usually only a few terms are really relevant in such a representation. It is a challenge to infer the best sparse polynomial chaos expansion of a given model input/output data set. In the present article, sparse polynomial chaos expansions are investigated for global sensitivity analysis of computer model responses. A new Bayesian approach is proposed to perform this task, based on the Kashyap information criterion for model selection. The efficiency of the proposed algorithm is assessed on several benchmarks before applying the algorithm to identify the most relevant inputs of a double-diffusive convection model. specialized Global sensitivity analysis Sobol' indices Sparse polynomial chaos expansion Bayesian model averaging Kashyap information criterion Double diffusive convection 020 318 474-496 2017 0045-7825 https://www.documentation.ird.fr/hor/fdi:010069521 10.1016/j.cma.2017.01.033 0045-7825 [F B010069521] https://www.documentation.ird.fr/hor/fdi:010069521 https://www.documentation.ird.fr/intranet/publi/2017/05/010069521.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2017-06-01 2023-07-11 fdi:010069521 fre