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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Metropolis-Hastings algorithms for Bayesian inference in Hilbert spaces
Metropolis-Hastings algorithms for Bayesian inference in Hilbert spacesAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact INI IT. This talk has been canceled/deleted In this talk we consider the Bayesian approach to inverse problems and infer uncertain coefficients in elliptic PDEs given noisy observations of the associated solution. After provinding a short introduction to this approach and illustrating it at a real-world groundwater flow problem, we focus on Metropolis-Hastings (MH) algorithms for approximate sampling of the resulting posterior distribution. These methods used to suffer from a high dimensional state space or a highly concentrated posterior measure, respectively. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:This talk is not included in any other list Note that ex-directory lists are not shown. |
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