Quantification of observed prior and likelihood information in parametric Bayesian modeling

Giri Gopalan

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arXiv · 1511.01214

Quantification of observed prior and likelihood information in parametric Bayesian modeling

Giri Gopalan

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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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Giri Gopalan. 2017-09-07. Quantification of observed prior and likelihood information in parametric Bayesian modeling. https://arxiv.org/abs/1511.01214

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