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Document type:
Konferenzbeitrag 
Author(s):
I. Franck, P.S. Koutsourelakis 
Title:
Variational Bayesian Formulations for High-Dimensional Inverse Problems 
Absract (alternative):
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...    »
 
Editor:
SIAM 
Book / Congress title:
SIAM - Computational Science and Engineering 
Date of congress:
2015 
Date of publication:
18.03.2015 
Year:
2015 
Year / month:
2015-03