The Library
A Bayesian method for calibration and aggregation of expert judgement
Tools
Tools
Tools
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
|
PDF
WRAP-Bayesian-method-calibration-aggregation-expert-judgement-Hartley-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1806Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.ijar.2020.12.007
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: |
|
||||||||
| 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 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
