On Bayesian analysis of mixtures with an unknown number of components - Discussion

Robert, C.P. and Aitkin, M. and Cox, David and Stephens, M. and Polymenis, A. and Gilks, W.R. and Nobile, A. and Hodgson, M. and Ohagan, A. and Longford, Nicholas T. and Dawid, A.P. and Atkinson, A.C. and Bernardo, J.M. and Besag, J. and Brooks, Stephen P. and Byers, S. and Raftery, A. and Celeux, G. and Cheng, Russell C.H. and Liu, Steve Wenbin and Chien, Y.H. and George, E.I. and Cressie, Noel A. and Huang, H.C. and Gruet, M.A. and Heath, S.C. and Jennison, C. and Lawson, Andrew B. and Clark, A. and McLachlan, Geoff and Peel, D. and Mengersen, K. and George, A. and Philippe, A. and Roeder, K. and Wasserman, Larry A. and Schlattmann, P. and Bohning, D. and Titterington, D.M. and Tong, Howell and West, M.J. (1997) On Bayesian analysis of mixtures with an unknown number of components - Discussion. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 59 (4). pp. 758-792. ISSN 1369-7412. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1111/1467-9868.00095

Abstract

New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science
Depositing User: T.J. Sango
Date Deposited: 18 May 2009 08:56
Last Modified: 18 Jul 2014 14:09
Resource URI: http://kar.kent.ac.uk/id/eprint/17917 (The current URI for this page, for reference purposes)
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