Improved Search for Structure Learning of Large Bayesian Networks

The problem of Bayesian network structure learning is defined as an optimization problem over the space of all possible network structures. For low-dimensional data, optimal structure learning approaches exist. For high-dimensional data, structure learning remains a significant challenge. Most commonly, approaches to high-dimensional structure learning employ a reduced search space and apply hill climbing methods to find high-scoring network structures. But even the reduced search space contains many local optima so that local search methods are unable to find near-optimal network structures. Instead of focusing on search space reduction, as most of the previous work in this area, we propose to replace the greedy search schemes with more effective search methods. We show that for high-dimensional data the proposed search method finds significantly better structures than other leading approaches to structure learning.