The University of Southampton
University of Southampton Institutional Repository

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

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.

Full text not available from this repository.

Citation

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).

More information

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

Identifiers

Local EPrints ID: 54073
URI: http://eprints.soton.ac.uk/id/eprint/54073
ISSN: 0960-3174
PURE UUID: a308a381-64e3-484e-b153-fd2499caa77a
ORCID for Sujit K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598

Catalogue record

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

Export record

Altmetrics

Contributors

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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×