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Dokumenttyp:
Konferenzbeitrag 
Autor(en):
I. Franck, P.S. Koutsourelakis 
Titel:
Variational Bayesian Formulations for High-Dimensional Inverse Problems 
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 
Konferenzort:
Salt Lake City, Utah, USA 
Datum der Konferenz:
2015 
Publikationsdatum:
18.03.2015 
Jahr:
2015 
Jahr / Monat:
2015-03