Learning Bayesian networks with a hybrid convergent method

During the past few years, a variety of methods have been developed for learning probabilistic networks from data, among which the heuristic single link forward or backward searches are widely adopted to reduce the search space. A major drawback of these search heuristics is that they can not guarantee to converge to the right networks even if a sufficiently large data set is available. This motivates us to explore an algorithm that will not suffer from this problem. We first identify an asymptotic property of different score metrics, based on which we then present a hybrid learning method that can be proved to be asymptotically convergent. We show that the algorithm, when employing the information criterion and the Bayesian metric, guarantees to converge in a very general way and is computationally feasible. Evaluation of the algorithm with simulated data is given to demonstrate the capability of the algorithm