Evaluating the Explanatory Value of Bayesian Network Structure Learning Algorithms

Patrick Shaughnessy, Gary Livingston

This paper presents a technique for evaluating the degree of correctness of structural models produced by Bayesian network learning algorithms. In this method, (1) Bayesian networks are generated pseudo-randomly using a chosen model distribution; (2) data sets of various sizes are produced using the generated networks; (3) the data sets are passed to learning algorithms; and (4) the network structures output by the learning algorithms are compared to the original networks. In the past, similar methods have used individually hand-selected networks rather than generating large families of networks and have focused on data sets with a large number of cases. Sample results on several search-and-score algorithms are shown, and we discuss the use of domain-specific simulators for generating data which may be used to better evaluate the causal learning.

Subjects: 3.4 Probabilistic Reasoning; 9.1 Causality

Submitted: May 17, 2006

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