Learning Bayesian Networks: A Unification for Discrete and Gaussian
  Domains
  We examine Bayesian methods for learning Bayesian networks from a combination
of prior knowledge and statistical data. In particular, we unify the approaches
we presented at last year's conference for discrete and Gaussian domains. We
derive a general Bayesian scoring metric, appropriate for both domains. We then
use this metric in combination with well-known statistical facts about the
Dirichlet and normal--Wishart distributions to derive our metrics for discrete
and Gaussian domains.
