A survey of Bayesian Network structure learning
Queen Mary University of London · Turing Institute · +2 more institutions
Abstract
Abstract Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology, economics and the social sciences. This is especially true in real-world areas where we seek to answer complex questions based on hypothetical evidence to determine actions for intervention. However, determining the graphical structure of a BN remains a major challenge, especially when modelling a problem under causal assumptions. Solutions to this problem include the automated discovery of BN graphs from data, constructing them based on expert knowledge, or a combination of the two. This paper provides a comprehensive…
Citation impact
- FWCI
- 50.95
- Percentile
- 100%
- References
- 199
Authors
5- NKNeville K. KitsonCorresponding
Queen Mary University of London
- ACAnthony C. Constantinou
Turing Institute, Queen Mary University of London, British Library, The Alan Turing Institute
- ZGZhigao Guo
Queen Mary University of London
- YLYang Liu
Queen Mary University of London
- KCKiattikun Chobtham
Queen Mary University of London
Topics & keywords
- Computer science
- Graphical model
- Bayesian network
- Machine learning
- Artificial intelligence
- Data science
- Consistency (knowledge bases)
- Causal structure
- Good health and well-being