Preprint Posting Date2015-11-02
Online Publication Date2016-02-13
Print Publication Date2016
Permanent link to this recordhttp://hdl.handle.net/10754/596466
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AbstractIn a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
CitationMatthies, H. G., Zander, E., Rosić, B. V., Litvinenko, A., & Pajonk, O. (2016). Inverse Problems in a Bayesian Setting. Computational Methods for Solids and Fluids, 245–286. doi:10.1007/978-3-319-27996-1_10
PublisherSpringer Science and Business Media LLC