Nonlinear methods for inverse statistical problems

Pierre Barbillon, Gilles Celeux, Agnès Grimaud, Yannick Lefebvre and Étienne De Rocquigny

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 132-142

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.

Keywords: Uncertainty; modelling; Nonlinear; approximation; Kriging; Stochastic; algorithm (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:132-142

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