Abstract
A Bayesian network (BN) is a probabilistic graphical model used in artificial intelligence research and application, providing a theoretical framework for machine learning. Based on Bayes’ theorem, BN is a directed acyclic graph (DAG) that connects variables using conditional probability. In recent years, significant progress has been made in this subject. By reviewing current relevant literature, this paper summarizes the most recent status of BN structure learning research. BN structure learning techniques are classified into three types: constraint-based learning methods, score-based learning methods, and hybrid learning methods. Furthermore, this paper summarizes and examines the BN structure learning technique with missing data. Finally, further research directions and topics are proposed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
