Using New Data to Refine a Bayesian Network
  We explore the issue of refining an existent Bayesian network structure using
new data which might mention only a subset of the variables. Most previous
works have only considered the refinement of the network's conditional
probability parameters, and have not addressed the issue of refining the
network's structure. We develop a new approach for refining the network's
structure. Our approach is based on the Minimal Description Length (MDL)
principle, and it employs an adapted version of a Bayesian network learning
algorithm developed in our previous work. One of the adaptations required is to
modify the previous algorithm to account for the structure of the existent
network. The learning algorithm generates a partial network structure which can
then be used to improve the existent network. We also present experimental
evidence demonstrating the effectiveness of our approach.
