Honest Bayesian confidence sets for the L2-norm

B.T. Szabó, A.W. Vaart, van der, J.H. Zanten, van

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)


We investigate the problem of constructing Bayesian credible sets that are honest and adaptive for the L^2-loss over a scale of Sobolev classes with regularity ranging between [D,2D], for some given D in the context of the signal-in-white-noise model. We consider a scale of prior distributions indexed by a regularity hyper-parameter and choose the hyper-parameter both by marginal likelihood empirical Bayes and by hierarchical Bayes method, respectively. Next we consider a ball centred around the corresponding posterior mean with prescribed posterior probability. We show by theory and examples that both the empirical Bayes and the hierarchical Bayes credible sets give misleading, overconfident uncertainty quantification for certain oddly behaving truth. Then we construct a new empirical Bayes method based on risk estimation, which provides the correct uncertainty quantification and optimal size. Keywords: Credible sets; Coverage; Uncertainty quantification
Original languageEnglish
Pages (from-to)36-51
Number of pages16
JournalJournal of Statistical Planning and Inference
Publication statusPublished - 2015


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