September 2020
October 2020
Bayesian inverse problems, Gaussian processes, and partial differential equations
Richard Nickl (University of Cambridge)
online
Abstract: The Bayesian approach to inverse problems has become very popular in the last decade after seminal work by Andrew Stuart (2010) and collaborators. Particularly in non-linear applications with PDEs and when using Gaussian process priors, this can leverage powerful MCMC methodology to tackle difficult high-dimensional and non-convex inference problems. Little is known in terms of rigorous performance guarantees for such algorithms. After laying out the main ideas behind Bayesian inversion, we will discuss recent progress providing both statistical and…
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Gábor Lugosi (Pompeu Fabra University )
online
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Carola-Bibiane Schönlieb (University of Cambridge )
online
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Jose Blanchet (Stanford University)
online
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Alessandro Rinaldo (Carnegie Mellon University)
online
TBD
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Daniela Witten (University of Washington)
online
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Mary Wootters (Stanford University )
online
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Arnaud Doucet (University of Oxford)
online
TBD
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