Walker, S.G. and Damien, P. and Lenk, P.J. (2004) On priors with a Kullback-Leibler property. Journal of the American Statistical Association, 99 (486). pp. 404-408. ISSN 0162-1459.
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In this paper, we highlight properties of Bayesian models in which the prior puts positive mass on all Kullback-Leibler neighborhoods of all densities. These properties are concerned with model choice via the Bayes factor, density estimation and the maximization of expected utility for decision problems. In four illustrations we focus on the Bayes factor and show that whatever models are being compared, the [log(Bayes factor)]/[sample size] converges to a non-random number which has a nice interpretation. A parametric versus semiparametric model comparison provides a fifth illustration.
|Subjects:||Q Science > QA Mathematics (inc Computing science)|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Judith Broom|
|Date Deposited:||02 Oct 2008 17:04|
|Last Modified:||14 Jan 2010 14:41|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/10584 (The current URI for this page, for reference purposes)|
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