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- W1573629697 abstract "Abstract. We study the Bayesian solution of a linear inverse problem in a separable Hilbert space setting with Gaussian prior and noise distribution. Our contribution is to propose a new Bayes estimator which is a linear and continuous estimator on the whole space and is stronger than the mean of the exact Gaussian posterior distribution which is only defined as a measurable linear transformation . Our estimator is the mean of a slightly modified posterior distribution called regularized posterior distribution . Frequentist consistency of our estimator and of the regularized posterior distribution is proved. A Monte Carlo study and an application to real data confirm good small‐sample properties of our procedure." @default.
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- W1573629697 date "2012-04-18" @default.
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- W1573629697 title "Regularized Posteriors in Linear Ill-Posed Inverse Problems" @default.
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- W1573629697 doi "https://doi.org/10.1111/j.1467-9469.2011.00784.x" @default.
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