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JSRI 2012, 9(1): 1-10 Back to browse issues page
Bayesian Optimum Design Criterion for Multi Models Discrimination
F Z. Labbaf, H Talebi *1
1- , h-talebi@sci.ui.ac.ir
Abstract:   (2601 Views)

The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an extension of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where the weights are corresponding probabilities of models to let them be true. We consider these probabilities coming from a Poisson distribution.

Keywords: Kulback-Leibler distance, discrimination, nested models, optimum design, optimality criterion.
Full-Text [PDF 191 kb]   (703 Downloads)    
Type of Study: Research | Subject: General
Received: 2015/12/22 | Accepted: 2015/12/22 | Published: 2015/12/22
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Z. Labbaf F, Talebi H. Bayesian Optimum Design Criterion for Multi Models Discrimination. JSRI. 2012; 9 (1) :1-10
URL: http://jsri.srtc.ac.ir/article-1-66-en.html


Volume 9, Issue 1 (9-2012) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران (علمی - پژوهشی) Journal of Statistical Research of Iran JSRI
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