A bayesian predictive approach to determining the number of components in a mixture distribution
A bayesian predictive approach to determining the number of components in a mixture distribution
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.
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
297-305
Dey, D.K.
bd7eaa2e-9bfd-44d0-beb7-2f27f9277b90
Kuo, L.
f98f4276-2906-4625-8d0c-e2b3e8443288
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
December 1995
Dey, D.K.
bd7eaa2e-9bfd-44d0-beb7-2f27f9277b90
Kuo, L.
f98f4276-2906-4625-8d0c-e2b3e8443288
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
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), .
(doi:10.1007/BF00162502).
Abstract
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
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Statistics
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Local EPrints ID: 54073
URI: http://eprints.soton.ac.uk/id/eprint/54073
ISSN: 0960-3174
PURE UUID: a308a381-64e3-484e-b153-fd2499caa77a
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Date deposited: 05 Aug 2008
Last modified: 16 Mar 2024 03:15
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Author:
D.K. Dey
Author:
L. Kuo
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