  We develop simple methods for constructing likelihoods and parameter priors
for learning about the parameters and structure of a Bayesian network. In
particular, we introduce several assumptions that permit the construction of
likelihoods and parameter priors for a large number of Bayesian-network
structures from a small set of assessments. The most notable assumption is that
of likelihood equivalence, which says that data can not help to discriminate
network structures that encode the same assertions of conditional independence.
We describe the constructions that follow from these assumptions, and also
present a method for directly computing the marginal likelihood of a random
sample with no missing observations. Also, we show how these assumptions lead
to a general framework for characterizing parameter priors of multivariate
distributions.
