The Library
Balancing the elicitation burden and the richness of expert input when quantifying discrete Bayesian networks
Tools
Tools
Tools
Barons, Martine J., Mascaro, Steven and Hanea, Anca (2021) Balancing the elicitation burden and the richness of expert input when quantifying discrete Bayesian networks. Risk Analysis . doi:10.1111/risa.13772 (In Press)
|
PDF
WRAP-Balancing-elicitation-burden-richness-expert-quantifying-discrete-Bayesian-networks-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1111/risa.13772
Abstract
Structured expert judgment (SEJ) is a method for obtaining estimates of uncertain quantities from groups of experts in a structured way designed to minimize the pervasive cognitive frailties of unstructured approaches. When the number of quantities required is large, the burden on the groups of experts is heavy, and resource constraints may mean that eliciting all the quantities of interest is impossible. Partial elicitations can be complemented with imputation methods for the remaining, unelicited quantities. In the case where the quantities of interest are conditional probability distributions, the natural relationship between the quantities can be exploited to impute missing probabilities. Here we test the Bayesian intelligence interpolation method and its variations for Bayesian network conditional probability tables, called “InterBeta.” We compare the various outputs of InterBeta on two cases where conditional probability tables were elicited from groups of experts. We show that interpolated values are in good agreement with experts' values and give guidance on how InterBeta could be used to good effect to reduce expert burden in SEJ exercises.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||
| Divisions: | Faculty of Science > Statistics | ||||||
| Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Multilevel models (Statistics), Decision making -- Mathematical models | ||||||
| Journal or Publication Title: | Risk Analysis | ||||||
| Publisher: | Wiley-Blackwell Publishing Ltd. | ||||||
| ISSN: | 0272-4332 | ||||||
| Official Date: | 19 June 2021 | ||||||
| Dates: |
|
||||||
| DOI: | 10.1111/risa.13772 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Access rights to Published version: | Open Access | ||||||
| RIOXX Funder/Project Grant: |
|
||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
