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Bayesian priors from loss matching

Brown, Philip J., Walker, Stephen G. (2012) Bayesian priors from loss matching. International Statistical Review, 80 (1). pp. 60-82. ISSN 0306-7734. (doi:10.1111/j.1751-5823.2011.00176.x) (KAR id:31314)

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This paper is concerned with the construction of prior probability measures for parametric families of densities where the framework is such that only beliefs or knowledge about a single observable data point is required. We pay particular attention to the parameter which minimizes a measure of divergence to the distribution providing the data. The prior distribution reflects this attention and we discuss the application of the Bayes rule from this perspective. Our framework is fundamentally non-parametric and we are able to interpret prior distributions on the parameter space using ideas of matching loss functions, one of which is coming from the data model and the other from the prior.

Item Type: Article
DOI/Identification number: 10.1111/j.1751-5823.2011.00176.x
Additional information: With Discussion
Uncontrolled keywords: Conjugate prior; Dirichlet process; Kullback-Leibler divergence; Loss function; Model choice; M-open; Prior distribution; Self-information loss.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Philip Brown
Date Deposited: 05 Oct 2012 13:39 UTC
Last Modified: 16 Nov 2021 10:09 UTC
Resource URI: (The current URI for this page, for reference purposes)
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