Using Causal Information and Local Measures to Learn Bayesian Networks

Using Causal Information and Local Measures to Learn Bayesian Networks (1993)

by Wai Lam , Fahiem Bacchus

Citations:

31 - 2 self

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Datum	Value	Source
TITLE	Using Causal Information and Local Measures to Learn Bayesian Networks	user correction
AUTHOR NAME	Wai Lam	user correction
AUTHOR AFFIL	Department of Computer Science; University ofWaterloo	user correction
AUTHOR ADDR	Waterloo, Ontario,; Canada, N2L 3G1	user correction
AUTHOR NAME	Fahiem Bacchus	user correction
ABSTRACT	In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the learned network against its practical usefulness. In this paper we present some new results that have arisen from our work. In particular, we present a new local way of computing the description length. This allows us to make significant improvements in our search algorithm. In addition, we modify our algorithm so that it can take into account partial domain information that might be provided by a domain expert. The local computation of description length also opens the door for local refinement of an existent network. The feasibility of our approach is demonstrated by experiments involving networks of a practical size.	user correction
YEAR	1993	user correction
CITATIONS	23 found	ParsCit 1.0