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Expert judgement combination using moment methods

Wisse, B.W. and Bedford, T.J. and Quigley, J.L. (2008) Expert judgement combination using moment methods. Reliability Engineering and System Safety, 93 (5). pp. 675-686. ISSN 0951-8320

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Abstract

Moment methods have been employed in decision analysis, partly to avoid the computational burden that decision models involving continuous probability distributions can suffer from. In the Bayes linear (BL) methodology prior judgements about uncertain quantities are specified using expectation (rather than probability) as the fundamental notion. BL provides a strong foundation for moment methods, rooted in work of De Finetti and Goldstein. The main objective of this paper is to discuss in what way expert assessments of moments can be combined, in a non-Bayesian way, to construct a prior assessment. We show that the linear pool can be justified in an analogous but technically different way to linear pools for probability assessments, and that this linear pool has a very convenient property: a linear pool of experts' assessments of moments is coherent if each of the experts has given coherent assessments. To determine the weights of the linear pool we give a method of performance based weighting analogous to Cooke's classical model and explore its properties. Finally, we compare its performance with the classical model on data gathered in applications of the classical model.

Item type: Article
ID code: 4875
Keywords: expert judgement, moment methods, reliability engineering, system safety, decision analysis, Management. Industrial Management, Applied Mathematics, Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality
Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
Department: Strathclyde Business School > Management Science
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 19 Jan 2008
    Last modified: 04 Sep 2014 18:37
    URI: http://strathprints.strath.ac.uk/id/eprint/4875

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