Construction of Bayesian network structures from data: A brief survey and an efficient algorithm

AbstractPrevious algorithms for the recovery of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required on 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-test-based method. Results of the evaluation of the algorithm on a number of databases (e.g., alarm, led, and soybean) are presented. We also discuss some algorithm performance issues and open problems