Quantification of observed prior and likelihood information in parametric Bayesian modeling

2015/11/04 by Giri Gopalan, Gopalan, Giri
Computer Science · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (stat.ML) #Methodology (stat.ME) #cs.IT #math.IT #stat.AP #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.1511.01214

Abbreviated and edited conference version

arxiv created 2017/09/07 · arxiv updated 2017/09/08

Abstract

Two data-dependent information metrics are developed to quantify the information of the prior and likelihood functions within a parametric Bayesian model, one of which is closely related to the reference priors from Berger, Bernardo, and Sun, and information measure introduced by Lindley. A combination of theoretical, empirical, and computational support provides evidence that these information-theoretic metrics may be useful diagnostic tools when performing a Bayesian analysis.

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