Adaptive regularization of noisy linear inverse problems

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    Abstract

    In the Bayesian modeling framework there is a close relation between regularization and the prior distribution over parameters. For prior distributions in the exponential family, we show that the optimal hyper-parameter, i.e., the optimal strength of regularization, satisfies a simple relation: The expectation of the regularization function, i.e., takes the same value in the posterior and prior distribution. We present three examples: two simulations, and application in fMRI neuroimaging.
    Original languageEnglish
    Title of host publicationEusipco
    Publication date2006
    Publication statusPublished - 2006

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