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• W2296087295 abstract "We study the use of Gaussian process emulators to approximate the parameter-to-observation map or the negative log-likelihood in Bayesian inverse problems. We prove error bounds on the Hellinger distance between the true posterior distribution and various approximations based on the Gaussian process emulator. Our analysis includes approximations based on the mean of the predictive process, as well as approximations based on the full Gaussian process emulator. Our results show that the Hellinger distance between the true posterior and its approximations can be bounded by moments of the error in the emulator. Numerical results confirm our theoretical findings." @default.
• W2296087295 created "2016-06-24" @default.
• W2296087295 creator A5032114852 @default.
• W2296087295 creator A5045167961 @default.
• W2296087295 date "2016-03-07" @default.
• W2296087295 modified "2023-09-27" @default.
• W2296087295 title "Posterior Consistency for Gaussian Process Approximations of Bayesian Posterior Distributions" @default.
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• W2296087295 hasPublicationYear "2016" @default.
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