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Prior and candidate models in the Bayesian analysis of finite mixtures

Prior and candidate models in the Bayesian analysis of finite mixtures
Prior and candidate models in the Bayesian analysis of finite mixtures
This paper discusses the problem of fitting mixture models to input data. When an input stream is an amalgam of data from different sources then such mixture models must be used if the true nature of the data is to be properly represented. A key problem is then to identify the different components of such a mixture, and in particular to determine how many components there are. This is known to be a non-regular/non-standard problem in the statistical sense and is technically notoriously difficult to handle properly using classical inferential methods. We discuss a Bayesian approach and show that there is a theoretical basis why this approach might overcome the problem. We describe the Bayesian approach explicitly and give examples showing its application.
0780381319
392-398
Cheng, R.C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Currie, C.S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Cheng, R.C.H.
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Currie, C.S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a

Cheng, R.C.H. and Currie, C.S.M. (2003) Prior and candidate models in the Bayesian analysis of finite mixtures. 2003 Winter Simulation Conference. 07 - 10 Dec 2003. pp. 392-398 .

Record type: Conference or Workshop Item (Other)

Abstract

This paper discusses the problem of fitting mixture models to input data. When an input stream is an amalgam of data from different sources then such mixture models must be used if the true nature of the data is to be properly represented. A key problem is then to identify the different components of such a mixture, and in particular to determine how many components there are. This is known to be a non-regular/non-standard problem in the statistical sense and is technically notoriously difficult to handle properly using classical inferential methods. We discuss a Bayesian approach and show that there is a theoretical basis why this approach might overcome the problem. We describe the Bayesian approach explicitly and give examples showing its application.

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More information

Published date: 2003
Venue - Dates: 2003 Winter Simulation Conference, 2003-12-07 - 2003-12-10
Organisations: Operational Research

Identifiers

Local EPrints ID: 29632
URI: https://eprints.soton.ac.uk/id/eprint/29632
ISBN: 0780381319
PURE UUID: 36cf4cee-791a-4503-b1f7-bffe981c6616
ORCID for C.S.M. Currie: ORCID iD orcid.org/0000-0002-7016-3652

Catalogue record

Date deposited: 12 May 2006
Last modified: 08 Jan 2019 01:35

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Contributors

Author: R.C.H. Cheng
Author: C.S.M. Currie ORCID iD

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