Learning Bayesian Networks from Dependency Networks: A Preliminary Study

Learning Bayesian Networks from Dependency Networks: A Preliminary Study (2003)

by Geoff Hulten

Venue:	in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL
Citations:	4 - 1 self

Datum	Value	Source
TITLE	Learning Bayesian Networks from Dependency Networks: A Preliminary Study	INFERENCE
AUTHOR NAME	Geoff Hulten	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Washington	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA	SVM HeaderParse 0.2
ABSTRACT	In this paper we describe how to learn Bayesian networks from a summary of complete data in the form of a dependency network rather than from data directly. This method allows us to gain the advantages of both representations: scalable algorithms for learning dependency networks and convenient inference with Bayesian networks. Our approach is to use a dependency network as an “oracle ” for the statistics needed to learn a Bayesian network. We show that the general problem is NP-hard and develop a greedy search algorithm. We conduct a preliminary experimental evaluation and find that the prediction accuracy of the Bayesian networks constructed from our algorithm almost equals that of Bayesian networks learned directly from the data. 1	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
VENUE	in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	12 found	ParsCit 1.0