Convergence Rates for Bayesian Inversion

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• Hanne Kekkonen (University of Helsinki)
• Thursday 12 May 2016, 15:00-16:00
• MR 14, CMS.

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

Let us consider an indirect noisy measurement M of a physical quantity M = AU + delta E where the measurement M, noise E and the unknown U are treated as random variables and delta models the noise amplitude. We are interested to know what happens to the approximate solution of above when delta → 0. The analysis of small noise limit, also known as the theory of posterior consistency, has attracted a lot of interest in the last decade, however, much remains to be done. Developing a comprehensive theory is important since posterior consistency justifies the use of Bayesian approach the same way as convergence results do the use of regularisation techniques.

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

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