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Approximate marginalization of uninteresting unknowns in inverse problems

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If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

In the Bayesian inverse problems framework, all unknown parameters are treated as random variables and all uncertainties can be modeled systematically. Recently, the approximation error approach has been proposed for handling modeling errors due to unknown nuisance parameters and model reduction. In this approach, approximate marginalization of the modeling errors is carried out before the estimation of the interesting variables. In this talk, we describe the approximation error approach and present computational examples that are related to local X-ray tomography imaging and electrical impedance tomography.

This talk is part of the Applied and Computational Analysis series.

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