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Frequentist coverage of adaptive nonparametric Bayesian credible sets

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We investigate the frequentist coverage of Bayesian credible sets in a nonparametric setting. We consider a scale of priors of varying regularity and choose the regularity by an empirical Bayes method. Next we consider a central set of prescribed posterior probability in the posterior distribution of the chosen regularity. We show that such an adaptive Bayes credible set gives correct uncertainty quantification of `polished tail’ parameters, in the sense of high probability of coverage of such parameters. On the negative side we show by theory and example that adaptation of the prior necessarily leads to gross and haphazard uncertainty quantification for some true parameters that are still within the Sobolev regularity scale.

This is a joint work with Aad van der Vaart and Harry van Zanten

This talk is part of the Machine Learning @ CUED series.

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