Partial Order MCMC for Structure Discovery in Bayesian Networks

Understand

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.