  The structure of a Bayesian network encodes most of the information about the
probability distribution of the data, which is uniquely identified given some
general distributional assumptions. Therefore it's important to study the
variability of its network structure, which can be used to compare the
performance of different learning algorithms and to measure the strength of any
arbitrary subset of arcs.
  In this paper we will introduce some descriptive statistics and the
corresponding parametric and Monte Carlo tests on the undirected graph
underlying the structure of a Bayesian network, modeled as a multivariate
Bernoulli random variable.
