A Hybrid Convergent Method for Learning Probabilistic Networks

During 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 a new algorithm that will not suffer from this problem. In this paper, 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, guarantee 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.

• Type: Conference
• Year: 1998
• Where: AI
• Authors: Jun Liu, Kuo-Chu Chang, Jing Zhou
• Keywords: AI 1998, Artificial Intelligence, Heuristic Single Link, Hybrid Learning Method, Probabilistic Networks