Bayesian models for sparse probability tables

Smith, Jim Q. and Queen, Catriona M. (1996) Bayesian models for sparse probability tables. Annals of Statistics, 24 (5). pp. 2178-2198. ISSN 0090-5364. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1214/aos/1069362316

Abstract

We wish to make inferences about the conditional probabilities p(y/x), many of which are zero, when the distribution of X is unknown and one observes only a multinomial sample of the Y variates. To do this, fixed likelihood ratio models and quasi-incremental distributions are defined. It is shown that quasi-incremental distributions are intimately linked to decomposable graphs and that these graphs can guide us to transformations of X and Y which admit a conjugate Bayesian analysis on a reparametrization of the conditional probabilities of interest.

Item Type: Article
Uncontrolled keywords: Bayesian probability estimation; constraint graph; contingency tables; decomposable graph; generalized Dirichlet distributions; separation of likelihood
Subjects: H Social Sciences > HA Statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: P. Ogbuji
Date Deposited: 27 May 2009 08:05
Last Modified: 07 Apr 2014 13:17
Resource URI: http://kar.kent.ac.uk/id/eprint/18509 (The current URI for this page, for reference purposes)
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