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Bayesian aspects of some nonparametric problems
2000
Annals of Statistics
We study the Bayesian approach to nonparametric function estimation problems such as nonparametric regression and signal estimation. We consider the asymptotic properties of Bayes procedures for conjugate (= Gaussian) priors. We show that so long as the prior puts nonzero measure on the very large parameter set of interest then the Bayes estimators are not satisfactory. More specifically, we show that these estimators do not achieve the correct minimax rate over norm bounded sets in the
doi:10.1214/aos/1016218229
fatcat:lyl5s6jyqbbgfggxc6s462uaya