Bayesian network approach to multinomial parameter learning using data and expert judgments
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 396
- Type
- D - Journal article
- DOI
-
10.1016/j.ijar.2014.02.008
- Title of journal
- International Journal of Approximate Reasoning
- Article number
- 5
- First page
- 1252
- Volume
- 55
- Issue
- 5
- ISSN
- 0888-613X
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- 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
-
2
- Research group(s)
-
-
- Citation count
- 40
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Work was incorporated into AgenaRisk - the Bayesian network software which has >10,000 commercial and academic users (over 4500 new users since 2014). Enables users to build models more efficiently, encoding real world knowledge about a problem directly into the learning model as constraints on how parameters are learnt. AgenaRisk is used to: model and predict cybersecurity risk; to evaluate safety of medical devices; support medical decision making for trauma patients; support planning and decision making in major third world aid projects.
Helped secure major project funding e.g £1.6million EPSRC Grant EP/P009964/1 (PAMBAYESIAN) and £386K Leverhulme Trust Research Grant RPG-2016-118."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -