Nonparametric Bayesian Clay for Robust Decision Bricks | Zendy

Christian P. Robert | Zendy; Judith Rousseau | Zendy

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Nonparametric Bayesian Clay for Robust Decision Bricks

Author(s) -

Christian P. Robert,

Judith Rousseau

Publication year - 2016

Publication title -

statistical science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.204

H-Index - 108

eISSN - 2168-8745

pISSN - 0883-4237

DOI - 10.1214/16-sts567

Subject(s) - bayesian probability , robustness (evolution) , nonparametric statistics , wonder , computer science , anchoring , econometrics , watson , mathematics , artificial intelligence , machine learning , mathematical economics , epistemology , psychology , philosophy , social psychology , gene , biochemistry , chemistry

International audienceThis note discusses Watson and Holmes (2016) and their proposals towards more robust Bayesian decisions. While we acknowledge and commend the authors for setting new and all-encompassing principles of Bayesian robustness, and we appreciate the strong anchoring of those within a decision-theoretic referential, we remain uncertain as to which extent such principles can be applied outside binary decisions. We also wonder at the ultimate relevance of Kullback-Leibler neighbourhoods to characterise robustness and favour extensions along non-parametric axes

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