A Bayesian Network Scoring Metric That Is Based On Globally Uniform
  Parameter Priors
  We introduce a new Bayesian network (BN) scoring metric called the Global
Uniform (GU) metric. This metric is based on a particular type of default
parameter prior. Such priors may be useful when a BN developer is not willing
or able to specify domain-specific parameter priors. The GU parameter prior
specifies that every prior joint probability distribution P consistent with a
BN structure S is considered to be equally likely. Distribution P is consistent
with S if P includes just the set of independence relations defined by S. We
show that the GU metric addresses some undesirable behavior of the BDeu and K2
Bayesian network scoring metrics, which also use particular forms of default
parameter priors. A closed form formula for computing GU for special classes of
BNs is derived. Efficiently computing GU for an arbitrary BN remains an open
problem.
