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A bayesian predictive approach to determining the number of components in a mixture distribution

Dey, D.K., Kuo, L. and Sahu, Sujit K. (1995) A bayesian predictive approach to determining the number of components in a mixture distribution Statistics and Computing, 5, (4), pp. 297-305. (doi:10.1007/BF00162502).

Record type: Article


This paper describes a Bayesian approach to mixture modelling and a method based on predictive distribution to determine the number of components in the mixtures. The implementation is done through the use of the Gibbs sampler. The method is described through the mixtures of normal and gamma distributions. Analysis is presented in one simulated and one real data example. The Bayesian results are then compared with the likelihood approach for the two examples.

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Published date: December 1995
Keywords: bootstrap procedures, conditional predictive ordinate, gamma mixtures, Gibbs sampler, likelihood ratio (LR) statistic, Metropolis algorithm, Monte Carlo methods, normal mixtures, predictive distribution, pseudo Bayes factor
Organisations: Statistics


Local EPrints ID: 54073
ISSN: 0960-3174
PURE UUID: a308a381-64e3-484e-b153-fd2499caa77a
ORCID for Sujit K. Sahu: ORCID iD

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Date deposited: 05 Aug 2008
Last modified: 17 Jul 2017 14:36

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Author: D.K. Dey
Author: L. Kuo
Author: Sujit K. Sahu ORCID iD

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