Partial Order MCMC for Structure Discovery in Bayesian Networks
ABSTRACT We present a new Markov chain Monte Carlo method for estimating posterior
probabilities of structural features in Bayesian networks. The method draws
samples from the posterior distribution of partial orders on the nodes; for
each sampled partial order, the conditional probabilities of interest are
computed exactly. We give both analytical and empirical results that suggest
the superiority of the new method compared to previous methods, which sample
either directed acyclic graphs or linear orders on the nodes.