Quantification of observed prior and likelihood information in parametric Bayesian modeling
Machine Learning
2017-09-08 v9 Information Theory
math.IT
Applications
Methodology
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.
Keywords
Cite
@article{arxiv.1511.01214,
title = {Quantification of observed prior and likelihood information in parametric Bayesian modeling},
author = {Giri Gopalan},
journal= {arXiv preprint arXiv:1511.01214},
year = {2017}
}
Comments
Abbreviated and edited conference version