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Probabilistic inversion of expert judgments in the quantification of model uncertainty

Bedford, T.J. and Kraan, B. (2005) Probabilistic inversion of expert judgments in the quantification of model uncertainty. Management Science, 51 (6). pp. 995-1006. ISSN 0025-1909

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Abstract

Expert judgment is frequently used to assess parameter values of quantitative management science models, particularly in decision-making contexts. Experts can, however, only be expected to assess observable quantities, not abstract model parameters. This means that we need a method for translating expert assessed uncertainties on model outputs into uncertainties on model parameter values. This process is called probabilistic inversion. The probability distribution on model parameters obtained in this way can be used in a variety of ways, but in particular in an uncertainty analysis or as a Bayes prior. This paper discusses computational algorithms that have proven successful in various projects and gives examples from environmental modelling and banking. Those algorithms are given a theoretical basis by adopting a minimum information approach to modelling partial information. The role of minimum information is two-fold: It enables us to resolve the problem of nonuniqueness of distributions given the information we have, and it provides numerical stability to the algorithm by guaranteeing convergence properties.

Item type: Article
ID code: 4325
Keywords: multivariate distribution, uncertainty analysis, expert judgment, probabilistic inversion, credit scoring, environmental modeling, Management. Industrial Management, Statistics
Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
Social Sciences > Statistics
Department: Strathclyde Business School > Management Science
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 17 Oct 2007
    Last modified: 12 Mar 2012 10:40
    URI: http://strathprints.strath.ac.uk/id/eprint/4325

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