Robert, C.P.,
Aitkin, M.,
Cox, David,
Stephens, M.,
Polymenis, A.,
Gilks, W.R.,
Nobile, A.,
Hodgson, M.,
Ohagan, A.,
Longford, Nicholas T.,
and others.
Dawid, A.P.,
Atkinson, A.C.,
Bernardo, J.M.,
Besag, J.,
Brooks, Stephen P.,
Byers, S.,
Raftery, A.,
Celeux, G.,
Cheng, Russell C.H.,
Liu, Wenbin,
Chien, Y.H.,
George, E.I.,
Cressie, Noel A.,
Huang, H.C.,
Gruet, M.A.,
Heath, S.C.,
Jennison, C.,
Lawson, Andrew B.,
Clark, A.,
McLachlan, Geoff,
Peel, D.,
Mengersen, K.,
George, A.,
Philippe, A.,
Roeder, K.,
Wasserman, Larry A.,
Schlattmann, P.,
Bohning, D.,
Titterington, D.M.,
Tong, Howell,
and
West, M.J.
(hide)
(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.
(doi:10.1111/1467-9868.00095)
(The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
(KAR id:17917)
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.
University of Kent Author Information
Cheng, Russell C.H..
Liu, Wenbin.
Tong, Howell.
- Depositors only (login required):
https://orcid.org/0000-0001-5966-6235
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