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Title:

Variational Bayesian Formulations for High-Dimensional Inverse Problems

Document type:
Konferenzbeitrag
Author(s):
I. Franck, P.S. Koutsourelakis
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
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