Non linear methods for Inverse Statistical Problems

Pierre Barbillon 1 Gilles Celeux 1 Agnès Grimaud 2 Yannick Lefebvre 3 Etienne Rocquigny (de) 4
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : In the uncertainty treatment framework considered, the intrinsic variability of the inputs of a physical simulation model is modelled by a multivariate probability distribution. The objective is to identify this probability distribution-the dispersion of which is independent of the sample size since intrinsic variability is at stake-based on observation of some model outputs. Moreover, in order to limit the number of (usually burdensome) physical model runs inside the inversion algorithm to a reasonable level, a nonlinear approximation methodology making use of Kriging and a stochastic EM algorithm is presented. It is compared with iterated linear approximation on the basis of numerical experiments on simulated data sets coming from a simplified but realistic modelling of a dyke overflow. Situations where this nonlinear approach is to be preferred to linearisation are highlighted.
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Article dans une revue
Computational Statistics and Data Analysis, Elsevier, 2010, 55, pp.132-142. 〈10.1016/j.csda.2010.05.030〉
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https://hal.inria.fr/hal-00943678
Contributeur : Gilles Celeux <>
Soumis le : samedi 8 février 2014 - 12:18:34
Dernière modification le : mardi 24 avril 2018 - 01:18:41

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Pierre Barbillon, Gilles Celeux, Agnès Grimaud, Yannick Lefebvre, Etienne Rocquigny (de). Non linear methods for Inverse Statistical Problems. Computational Statistics and Data Analysis, Elsevier, 2010, 55, pp.132-142. 〈10.1016/j.csda.2010.05.030〉. 〈hal-00943678〉

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