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• W2962953485 abstract "We consider the well-posedness of Bayesian inverse problems when the prior measure has exponential tails. In particular, we consider the class of convex (log-concave) probability measures which include the Gaussian and Besov measures as well as certain classes of hierarchical priors. We identify appropriate conditions on the likelihood distribution and the prior measure which guarantee existence, uniqueness, and stability of the posterior measure with respect to perturbations of the data. We also consider consistent approximations of the posterior such as discretization by projection. Finally, we present a general recipe for construction of convex priors on Banach spaces which will be of interest in practical applications where one often works with spaces such as $L^2$ or the continuous functions." @default.
• W2962953485 created "2019-07-30" @default.
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• W2962953485 date "2017-01-01" @default.
• W2962953485 modified "2023-10-16" @default.
• W2962953485 title "Well-Posed Bayesian Inverse Problems: Priors with Exponential Tails" @default.
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• W2962953485 doi "https://doi.org/10.1137/16m1076824" @default.
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