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A Bayesian method for calibration and aggregation of expert judgement

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Hartley, David and French, Simon (2021) A Bayesian method for calibration and aggregation of expert judgement. International Journal of Approximate Reasoning, 130 . pp. 192-225. doi:10.1016/j.ijar.2020.12.007

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Official URL: http://dx.doi.org/10.1016/j.ijar.2020.12.007

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Abstract

This paper outlines a Bayesian framework for structured expert judgement (sej) that can be utilised as an alternative to the traditional non-Bayesian methods (including the commonly used Cooke's Classical model [13]). We provide an overview of the structure of an expert judgement study and outline opinion pooling techniques noting the benefits and limitations of these approaches. Some new tractable Bayesian models are highlighted, before presenting our own model which aims to combine and enhance the best of these existing Bayesian frameworks. In particular: clustering, calibrating and then aggregating the experts' judgements utilising a Supra-Bayesian parameter updating approach combined with either an agglomerative hierarchical clustering or an embedded Dirichlet process mixture model. We illustrate the benefit of our approach by analysing data from a number of existing studies in the healthcare domain, specifically in the two contexts of health insurance and transmission risks for chronic wasting disease.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory , Human information processing -- Statistical methods , Risk assessment -- Statistical methods, Calibration
Journal or Publication Title: International Journal of Approximate Reasoning
Publisher: Elsevier
ISSN: 0888-613X
Official Date: March 2021
Dates:
DateEvent
March 2021Published
11 December 2020Available
1 December 2020Accepted
Volume: 130
Page Range: pp. 192-225
DOI: 10.1016/j.ijar.2020.12.007
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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