Prior and Posterior Linear Pooling for Combining Expert Opinions: Uses and Impact on Bayesian Networks—The Case of the Wayfinding Model

Article


Farr, Charisse, Ruggeri, Fabrizio and Mengersen, Kerrie. 2018. "Prior and Posterior Linear Pooling for Combining Expert Opinions: Uses and Impact on Bayesian Networks—The Case of the Wayfinding Model." Entropy: international and interdisciplinary journal of entropy and information studies. 20 (3), pp. 1-14. https://doi.org/10.3390/e20030209
Article Title

Prior and Posterior Linear Pooling for Combining Expert Opinions: Uses and Impact on Bayesian Networks—The Case of the Wayfinding Model

ERA Journal ID39951
Article CategoryArticle
AuthorsFarr, Charisse (Author), Ruggeri, Fabrizio (Author) and Mengersen, Kerrie (Author)
Journal TitleEntropy: international and interdisciplinary journal of entropy and information studies
Journal Citation20 (3), pp. 1-14
Article Number209
Number of Pages14
Year2018
Place of PublicationSwitzerland
ISSN1099-4300
Digital Object Identifier (DOI)https://doi.org/10.3390/e20030209
Web Address (URL)https://www.mdpi.com/1099-4300/20/3/209
Abstract

The use of expert knowledge to quantify a Bayesian Network (BN) is necessary when data is not available. This however raises questions regarding how opinions from multiple experts can be used in a BN. Linear pooling is a popular method for combining probability assessments from multiple experts. In particular, Prior Linear Pooling (PrLP), which pools opinions and then places them into the BN, is a common method. This paper considers this approach and an alternative pooling method, Posterior Linear Pooling (PoLP). The PoLP method constructs a BN for each expert, and then pools the resulting probabilities at the nodes of interest. The advantages and disadvantages of these two methods are identified and compared and the methods are applied to an existing BN, the Wayfinding Bayesian Network Model, to investigate the behavior of different groups of people and how these different methods may be able to capture such differences. The paper focusses on six nodes Human Factors, Environmental Factors, Wayfinding, Communication, Visual Elements of Communication and Navigation Pathway, and three subgroups Gender (Female, Male), Travel Experience (Experienced, Inexperienced), and Travel Purpose (Business, Personal), and finds that different behaviors can indeed be captured by the different methods.

Keywordsbayesian networks; linear pooling; posterior pooling; prior pooling; wayfinding; expert opinions
ANZSRC Field of Research 2020490501. Applied statistics
Byline AffiliationsQueensland University of Technology
Institute of Applied Mathematics and Information Technologies, Italy
Institution of OriginUniversity of Southern Queensland
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