An Algorithm for the Construction of Bayesian Network Structures from Data

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algorithm performance issues and open problems. 1 INTRODUCTION In very general terms, different methods of learning probabilistic network structures from data can be classified into three groups. Some of these methods are based on linearity and normality assumptions ([Glymour et. al., 87], [Pearl & Wermuth, 93]); others are more general but require extensive testing of independence relations ([Fung & Crawfo..