Combining expert opinions in prior elicitation

Albert, Isabelle, Donnet, Sophie, Guihenneuc-Jouyaux, Chantal, Low-Choy, Samantha, Mengersen, Kerrie, & Rousseau, Judith (2012) Combining expert opinions in prior elicitation. Bayesian Analysis, 7(3), pp. 503-532.

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We consider the problem of combining opinions from different experts in an explicitly model-based way to construct a valid subjective prior in a Bayesian statistical approach. We propose a generic approach by considering a hierarchical model accounting for various sources of variation as well as accounting for potential dependence between experts. We apply this approach to two problems. The first problem deals with a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. Two hierarchical levels of variation are considered (between and within experts) with a complex mathematical situation due to the use of an indirect probit regression. The second concerns the time taken by PhD students to submit their thesis in a particular school. It illustrates a complex situation where three hierarchical levels of variation are modelled but with a simpler underlying probability distribution (log-Normal).

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11 citations in Scopus
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21 citations in Web of Science®

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ID Code: 72989
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Bayesian statistics, Hierarchical model, Random effects, Risk assessment
DOI: 10.1214/12-BA717
ISSN: 1931-6690
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 International Society for Bayesian Analysis
Deposited On: 22 Jun 2014 23:18
Last Modified: 23 Jun 2014 23:07

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