  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.
