PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Diagnostics of prior-data conflict in applied Bayesian analysis
Nicolas Bousquet
Journal of Applied Statistics Volume 35, pp. 1011-1029, 2008.

Abstract

This article focused on the definition and the study of a binary Bayesian criterion which measures a statistical agreement between a subjective prior and data information. The setting of this work is concrete Bayesian studies. It is an alternative and a complementary tool to the method recently proposed by Evans and Moshonov, [M. Evans and H. Moshonov, Checking for Prior-data conflict, Bayesian Anal. 1 (2006), pp. 893–914]. Both methods try to help the work of the Bayesian analyst, from preliminary to the posterior computation. Our criterion is defined as a ratio of Kullback–Leibler divergences; two of its main features are to make easy the check of a hierarchical prior and be used as a default calibration tool to obtain flat but proper priors in applications. Discrete and continuous distributions exemplify the approach and an industrial case study in reliability, involving theWeibull distribution, is highlighted.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5367
Deposited By:Nicolas Bousquet
Deposited On:31 March 2009