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
2003
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, New Orleans, USA.
07 - 10 Dec 2003.
.
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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Published date: 2003
Venue - Dates:
2003 Winter Simulation Conference, New Orleans, USA, 2003-12-07 - 2003-12-10
Organisations:
Operational Research
Identifiers
Local EPrints ID: 29632
URI: http://eprints.soton.ac.uk/id/eprint/29632
ISBN: 0780381319
PURE UUID: 36cf4cee-791a-4503-b1f7-bffe981c6616
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Date deposited: 12 May 2006
Last modified: 09 Jan 2022 03:11
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