Abonnement à la biblothèque: Guest
Portail numérique Bibliothèque numérique eBooks Revues Références et comptes rendus Collections
International Journal for Uncertainty Quantification
Facteur d'impact: 4.911 Facteur d'impact sur 5 ans: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Imprimer: 2152-5080
ISSN En ligne: 2152-5099

Ouvrir l'accès

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2014011073
pages 73-98

BAYESIAN INFERENCE FOR INVERSE PROBLEMS OCCURRING IN UNCERTAINTY ANALYSIS

Shuai Fu
University Paris-Sud 11, France; EDF, R&D, France
Gilles Celeux
Inria Saclay-Ile-de-France
Nicolas Bousquet
EDF, R&D, France
Mathieu Couplet
EDF, R&D, France

RÉSUMÉ

The inverse problem considered here is the estimation of the distribution of a nonobserved random variable X, linked through a time-consuming physical model H to some noisy observed data Y. Bayesian inference is considered to account for prior expert knowledge on X in a small sample size setting. A Metropolis-Hastings-within-Gibbs algorithm is used to compute the posterior distribution of the parameters of the distribution of X through a data augmentation process. Since running H is quite expensive, this inference is achieved by a kriging emulator interpolating H from a numerical design of experiments (DOE). This approach involves several errors of different natures and, in this article, we pay effort to measure and reduce the possible impact of those errors. In particular, we propose to use the so-called DAC criterion to assess in the same exercise the relevance of the DOE and the prior distribution. After describing the calculation of this criterion for the emulator at hand, its behavior is illustrated on numerical experiments.


Articles with similar content:

SENSITIVITY INDICES FOR OUTPUT ON A RIEMANNIAN MANIFOLD
International Journal for Uncertainty Quantification, Vol.10, 2020, issue 4
F. Gamboa, Leonardo Moreno, R. Fraiman
The Multiple-Choice Sequential Decision Rule with Rejection of Unfortunate Hypotheses
Journal of Automation and Information Sciences, Vol.32, 2000, issue 10
Sergey Ya. Zhuk, Vladimir I. Kovalev
GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 6
Youssef Marzouk, Xun Huan
DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 1
Bert J. Debusschere, Habib N. Najm, Peter Thornton, Cosmin Safta, Khachik Sargsyan, Daniel Ricciuto
A Non Split Projection Strategy for Low Mach Number Flows
International Journal for Multiscale Computational Engineering, Vol.2, 2004, issue 3
P.P. Pebay, Habib N. Najm, J. G. Pousin