An information theory based approach to structure learning in Bayesian networks

Structure learning of Bayesian networks is a well-researched but computationally hard task. We present an algorithm that adopts an information theory based approach to learn structures of bayesian networks. Our algorithm also makes use of basic bayesian network concepts like D-separation and markov independence. We show that the proposed algorithm is capable of handling networks with a large number of variables. We present the applicability of the proposed algorithm on four datasets and also compare its performance and computational efficiency with other standard structure learning methods (Hill climbing, MCMC).