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Titel:

Variational Bayesian Formulations for High-Dimensional Inverse Problems

Dokumenttyp:
Konferenzbeitrag
Autor(en):
I. Franck, P.S. Koutsourelakis
Abstract (alternativ):
The present paper advocates a Variational Bayesian (VB) approach for approximating the posterior density in stochastic inverse problems. In contrast to sampling techniques (e.g. MCMC, SMC), VB requires much fewer forward evaluations. Furthermore it enables learning of a suitable lower-dimensional subspace where most of the posterior probability lies, and reducing dramatically the number of unknowns. We demonstrate the accuracy and efficiency of the proposed strategy in nonlinear problems and non...     »
Herausgeber:
SIAM
Kongress- / Buchtitel:
SIAM - Computational Science and Engineering
Datum der Konferenz:
2015
Publikationsdatum:
18.03.2015
Jahr:
2015
Jahr / Monat:
2015-03
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