Use of the AIC with the EM algorithm: A demonstration of a probability model selection technique
The problem of discriminating between two potential probability models, a Gaussian distribution and a mixture of Gaussian distributions, is considered. The focus of our interest is a case where the models are potentially non-nested and the parameters of the mixture model are estimated through the EM algorithm. The AIC, which is frequently used as a criterion for discriminating between non-nested models, is modified to work with the EM algorithm and is shown to provide a model selection tool for this situation. A particular problem involving an infinite mixture distribution known as Middleton`s Class A model is used to demonstrate the effectiveness and limitations of this method.
- Publication Date:
- OSTI Identifier:
- Report Number(s):
ON: DE95017252; TRN: 95:007225-0048
- Resource Type:
- Resource Relation:
- Conference: Imaging sciences workshop, Livermore, CA (United States), 15-16 Nov 1994; Other Information: PBD: 15 Nov 1994; Related Information: Is Part Of Imaging sciences workshop; Candy, J.V.; PB: 101 p.
- Research Org:
- Lawrence Livermore National Lab., CA (United States)
- Country of Publication:
- United States
- 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; GAUSS FUNCTION; COMPARATIVE EVALUATIONS; STATISTICAL MODELS; GAUSSIAN PROCESSES; MIXTURES
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