Using Causal Information and Local Measures to Learn Bayesian Networks
نویسندگان
چکیده
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 tradeo 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 signi cant improvements in our search algo rithm In addition we modify our algorithm so that it can take into account partial do main information that might be provided by a domain expert The local computation of description length also opens the door for lo cal re nement of an existent network The feasibility of our approach is demonstrated by experiments involving networks of a prac tical size Appears in Proceedings of Uncertainty in Arti cial Intelligence Pages
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