An Empirical Study of Bayesian Network Parameter Learning with Monotonic Influence Constraints
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 442
- Type
- D - Journal article
- DOI
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10.1016/j.dss.2016.05.001
- Title of journal
- Decision Support Systems
- Article number
- -
- First page
- 69
- Volume
- 87
- Issue
- -
- ISSN
- 1873-5797
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Body of work coming from Fenton’s €1.7m ERC Project (2014-18) that improves accuracy of decision-support models learned from a combination of expert judgement and data. This, and other algorithms underlying this work, was implemented in the AgenaRisk Bayesian network software, which has over 10,000 commercial and academic users worldwide. Helped secure other major project funding: £1.6million EPSRC Grant EP/P009964/1 (PAMBAYESIAN); £386K Leverhulme Trust Research Grant (RPG-2016-118); and numerous invited keynotes such as Fenton’s talk "Making Decisions with Less Data" at the Procter & Gamble Conference ‘Breaking the Barriers to Fast Cycle Consumer Learning’, Brussels, 26/6/15 (see https://tinyurl.com/y8ob7mlu for full list).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -