On the normalized maximum likelihood and Bayesian decision theory

Karabatsos, George and Walker, Stephen G. (2006) On the normalized maximum likelihood and Bayesian decision theory. Journal of Mathematical Psychology, 50 (6). pp. 517-520. ISSN 0022-2496. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Under the principle of minimum description length, the optimal predictive model maximizes the normalized maximum likelihood (NML). While the Bayesian approach to model selection aims to identify the model that best describes the (unknown) true distribution that generated a set of data, the NML approach to model selection makes no reference to a true distribution, and this is seen as a significant advantage of the latter approach. In contrast, this article shows that, for a specific choice of utility function, the NML approach is equivalent to a Bayesian model selection under the Bayesian boostrap and with a specific penalty function for model complexity. This new characterization uncovers some statistical issues about the NML approach. (c) 2006 Elsevier Inc. All rights reserved.

Item Type: Article
Uncontrolled keywords: Bayesian non-parametrics; normalized maximum likelihood; model selection
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: 05 Sep 2008 22:47
Last Modified: 25 Jun 2014 10:44
Resource URI: https://kar.kent.ac.uk/id/eprint/10527 (The current URI for this page, for reference purposes)
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