Open AccessNonparametric 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