Using Causal Information and Local Measures to Learn Bayesian Networks
  In previous work we developed a method of learning Bayesian Network models
from raw data. This method relies on the well known minimal description length
(MDL) principle. The MDL principle is particularly well suited to this task as
it allows us to tradeoff, in a principled way, the accuracy of the learned
network against its practical usefulness. In this paper we present some new
results that have arisen from our work. In particular, we present a new local
way of computing the description length. This allows us to make significant
improvements in our search algorithm. In addition, we modify our algorithm so
that it can take into account partial domain information that might be provided
by a domain expert. The local computation of description length also opens the
door for local refinement of an existent network. The feasibility of our
approach is demonstrated by experiments involving networks of a practical size.
