Learning Bayesian Networks From Dependency Networks: A Preliminary Study (2003) [3 citations — 0 self]
http://research.microsoft.com/conferences/AIStats2
http://research.microsoft.com/~dmax/publications/d
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author = {Geoff Hulten and David Maxwell Chickering and David Heckerman},
title = {Learning Bayesian Networks From Dependency Networks: A Preliminary Study},
booktitle = {in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL},
year = {2003}
}
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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.
