Learning Bayesian Networks from Data: An Information-Theory Based Approach

by Jie Cheng , Russell Greiner , Jonathan Kelly , David Bell , Weiru Liu

Citations:

67 - 4 self

User correction supplied by mph

Datum	Value	Source
TITLE	Learning Bayesian Networks from Data: An Information-Theory Based Approach	user correction
AUTHOR NAME	Jie Cheng	user correction
AUTHOR NAME	Russell Greiner	user correction
AUTHOR AFFIL	Department of Computing Science, University of Alberta	user correction
AUTHOR NAME	Jonathan Kelly	user correction
AUTHOR AFFIL	Department of Computing Science, University of Alberta	user correction
AUTHOR NAME	David Bell	user correction
AUTHOR AFFIL	Faculty of Informatics, University of Ulster	user correction
AUTHOR NAME	Weiru Liu	user correction
AUTHOR AFFIL	Faculty of Informatics, University of Ulster	user correction
ABSTRACT	This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.	SVM HeaderParse 0.1
CITATIONS	81 found	ParsCit 1.0