Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach

A. Charisse Farr, Kerrie Mengersen, Fabrizio Ruggeri, Daniel Simpson, Paul Wu and Prasad Yarlagadda

International Statistical Review, 2020, vol. 88, issue 2, 335-353

Abstract: Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasingly popular in a range of fields including engineering, ecology, computational biology, medical diagnosis and forensics. In most of these cases, the BNs are quantified using information from experts or from users' opinions. While this quantification is straightforward for one expert, there is still debate about how to represent opinions from multiple experts in a BN. This paper proposes the use of a measurement error model to achieve this. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the loss of the conditional independence structure of the BN and the provision of only a point estimate for the consensus. The proposed model is applied to a subnetwork (the three final nodes) of a larger BN about wayfinding in airports. It is shown that the approach performs well than do existing methods of combining opinions.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/insr.12350

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:istatr:v:88:y:2020:i:2:p:335-353

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0306-7734

Access Statistics for this article

International Statistical Review is currently edited by Eugene Seneta and Kees Zeelenberg

More articles in International Statistical Review from International Statistical Institute Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().