An Objective Bayesian Criterion to Determine Model Prior Probabilities

Villa, Cristiano (2015) An Objective Bayesian Criterion to Determine Model Prior Probabilities. Scandinavian Journal of Statistics, 42 (4). pp. 947-966. ISSN 0303-6898. E-ISSN 1467-9469.

Abstract

We discuss the problem of selecting among alternative parametric models within the Bayesian framework. For model selection problems which involve non-nested models, the common objective choice of a prior on the model space is the uniform distribution. The same applies to situations where the models are nested. It is our contention that assigning equal prior probability to each model is over simplistic. Consequently, we introduce a novel approach to objectively determine model prior probabilities conditionally on the choice of priors for the parameters of the models. The idea is based on the notion of the worth of having each model within the selection process. At the heart of the procedure is the measure of this worth using the Kullback--Leibler divergence between densities from di?erent models.

Item Type:	Article
Uncontrolled keywords:	Bayesian model selection, Kullback{Leibler divergence, objective Bayes, self-information loss
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Cristiano Villa
Date Deposited:	17 Feb 2015 16:06 UTC
Last Modified:	29 May 2019 14:12 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/47157 (The current URI for this page, for reference purposes)