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Eliciting Dirichlet and Connor–Mosimann prior distributions for multinomial models

Elfadaly, Fadlalla G. and Garthwaite, Paul H. (2013). Eliciting Dirichlet and Connor–Mosimann prior distributions for multinomial models. TEST, 22(4) pp. 628–646.

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This paper addresses the task of eliciting an informative prior distribution for multinomial models. We first introduce a method of eliciting univariate beta distributions for the probability of each category, conditional on the probabilities of other categories. Two different forms of multivariate prior are derived from the elicited beta distributions. First, we determine the hyperparameters of a Dirichlet distribution by reconciling the assessed parameters of the univariate beta conditional distributions. Although the Dirichlet distribution is the standard conjugate prior distribution for multinomial models, it is not flexible enough to represent a broad range of prior information. Second, we use the beta distributions to determine the parameters of a Connor–Mosimann distribution, which is a generalization of a Dirichlet distribution and is also a conjugate prior for multinomial models. It has a larger number of parameters than the standard Dirichlet distribution and hence a more flexible structure. The elicitation methods are designed to be used with the aid of interactive graphical user-friendly software.

Item Type: Journal Item
Copyright Holders: 2013 Sociedad de Estadística e Investigación Operativa
ISSN: 1863-8260
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetNot SetThe Open University (OU)
Keywords: Elicitation method; Prior distribution; Dirichlet distribution; Connor–Mosimann distribution; Multinomial model; Interactive graphical software
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 49484
Depositing User: Fadlalla Elfadaly
Date Deposited: 26 May 2017 09:08
Last Modified: 07 Dec 2018 10:51
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