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Title:

Variational Bayesian Approximations for Nonlinear Inverse Problems

Document type:
Konferenzbeitrag
Contribution type:
Vortrag / Präsentation
Author(s):
I. Franck, P.S. Koutsourelakis
Abstract:
Bayesian formulations represent one of the prominent approaches for addressing problems of model calibration. Existing Bayesian methodologies are hampered by the high-dimensionality of unknown model parameters and the high computational cost for inference. The present paper advocates a Variational Bayesian inference engine which exploits derivative information available from deterministic adjoint formulations. Furthermore we propose sparsity-enforcing priors that are suited for spatially-varying...     »
Book / Congress title:
SIAM Uncertainty Quantification
Year:
2014
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