@CONFERENCE\{IMM2006-04417,
    author       = "L. K. Hansen and K. H. Madsen and T. Lehn-Schi{\o}ler",
    title        = "Adaptive regularization of noisy linear inverse problems",
    year         = "2006",
    keywords     = "Bayes, regularization, inverse problems, fMRI",
    booktitle    = "Eusipco",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4417-full.html",
    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."
}