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University of Cambridge > Talks.cam > Probability Theory and Statistics in High and Infinite Dimensions > Adaptive nonparametric credible balls
Adaptive nonparametric credible ballsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact clc32. Credible sets are central sets in the support of a posterior probability distribution, of a prescribed posterior probability. They are widely used as means of uncertainty quantification in a Bayesian analysis. We investigate the frequentist coverage of such sets in a nonparametric Bayesian setup. We show by example that credible sets can be much too narrow and misleading, and next introduce a concept of `polished tail’ parameters for which credible sets are of the correct order. The latter concept can be seen as a generalisation of self-similar functions as considered in a recent paper by Giné. Joint work with Botond Szabó and Harry van Zanten. This talk is part of the Probability Theory and Statistics in High and Infinite Dimensions series. This talk is included in these lists:
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