Gert De Cooman
Coherent lower previsions in systems modelling: products and aggregation rules
De Cooman, Gert; Troffaes, Matthias C.M.
Abstract
We discuss why coherent lower previsions provide a good uncertainty model for solving generic uncertainty problems involving possibly conflicting expert information. We study various ways of combining expert assessments on different domains, such as natural extension, independent natural extension and the type-I product, as well as on common domains, such as conjunction and disjunction. We provide each of these with a clear interpretation, and we study how they are related. Observing that in combining expert assessments no information is available about the order in which they should be combined, we suggest that the final result should be independent of the order of combination. The rules of combination we study here satisfy this requirement.
Citation
De Cooman, G., & Troffaes, M. C. (2004). Coherent lower previsions in systems modelling: products and aggregation rules. Reliability Engineering & System Safety, 85(1-3), 113-134. https://doi.org/10.1016/j.ress.2004.03.007
Journal Article Type | Article |
---|---|
Publication Date | 2004-09 |
Deposit Date | Feb 29, 2008 |
Publicly Available Date | May 14, 2009 |
Journal | Reliability Engineering and System Safety |
Print ISSN | 0951-8320 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 85 |
Issue | 1-3 |
Pages | 113-134 |
DOI | https://doi.org/10.1016/j.ress.2004.03.007 |
Keywords | Expert information, Coherent lower previsions, Natural extension, Independence, Type-I product, Marginal extension, Conjunction, Disjunction. |
Public URL | https://durham-repository.worktribe.com/output/1554023 |
Files
Accepted Journal Article
(400 Kb)
PDF
You might also like
Lower previsions.
(2014)
Book
Imprecision in Statistical Theory and Practice
(2009)
Book
Introduction to Imprecise Probabilities
(2014)
Book
Bayesian Adaptive Selection Under Prior Ignorance
(2021)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search