Learning Bayesian networks using the constrained maximum a posteriori probability method
- Submitting institution
-
London South Bank University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 269637
- Type
- D - Journal article
- DOI
-
10.1016/j.patcog.2019.02.006
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 123
- Volume
- 91
- Issue
- -
- ISSN
- 0031-3203
- Open access status
- Access exception
- Month of publication
- February
- Year of publication
- 2019
- URL
-
https://www.sciencedirect.com/science/article/pii/S0031320319300706
- 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
-
3
- Research group(s)
-
B - Cognitive Systems Research Centre
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents systematically the first work of this kind in the area of learning parameters of discrete Bayesian networks when training data are scarce or incomplete using constrained maximum a posteriori method. The proposed strategies and approaches are applicable in a wide range of practical applications including sensitivity analysis, speech recognition, and bioinformatics. This research has enhanced the long-established academic collaboration between Northwestern Polytechnical University in China and LSBU and has led to joint applications to research funds, e.g., the Newton Fund of British Council.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -