  The main contribution of this article is a new prior distribution over
directed acyclic graphs, which gives larger weight to sparse graphs. This
distribution is intended for structured Bayesian networks, where the structure
is given by an ordered block model. That is, the nodes of the graph are objects
which fall into categories (or blocks); the blocks have a natural ordering. The
presence of a relationship between two objects is denoted by an arrow, from the
object of lower category to the object of higher category. The models
considered here were introduced in Kemp et al. (2004) for relational data and
extended to multivariate data in Mansinghka et al. (2006). The prior over graph
structures presented here has an explicit formula. The number of nodes in each
layer of the graph follow a Hoppe Ewens urn model.
  We consider the situation where the nodes of the graph represent random
variables, whose joint probability distribution factorises along the DAG. We
describe Monte Carlo schemes for finding the optimal aposteriori structure
given a data matrix and compare the performance with Mansinghka et al. (2006)
and also with the uniform prior.
