A framework to present Bayesian networks to domain experts and potential users

Date

2016-10-05

Author

Yet, Barbaros

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Knowledge and assumptions behind most Bayesian network models are often not clear to anyone other than their developers. This limits their use as decision support tools in clinical and legal domains where the outcomes of decisions can be critical. We propose a framework for representing knowledge supporting or conflicting with BN, and knowledge associated with factors that are relevant but excluded from the BN. The aim of this framework is to enable domain experts and potential users to browse, review, criticise and modify a BN model without having deep technical knowledge about BNs.Co-authors: Zane Perkins (Queen Mary University of London), Nigel Tai (The Royal London Hospital), William Marsh (Queen Mary University of London)

URI

https://sms.cam.ac.uk/media/2333190
https://hdl.handle.net/11511/84192

Conference Name

Probability and Statistics in Forensic Science - Isaac Newton Institute for Mathematical Sciences, (18 Temmuz - 21 Aralık 2016)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

B. Yet, “A framework to present Bayesian networks to domain experts and potential users,” presented at the Probability and Statistics in Forensic Science - Isaac Newton Institute for Mathematical Sciences, (18 Temmuz - 21 Aralık 2016), Cambridge, Kanada, 2016, Accessed: 00, 2021. [Online]. Available: https://sms.cam.ac.uk/media/2333190.