Using junction trees for structural learning of Bayesian networks

The learning Bayesian network (BN) structure from data is an NP-hard problem and still one of the most exciting challenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.