Efficient learning using constrained sufficient statistics
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2, 1999.
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of learning Bayesian network structure. This approach uses constraints imposed by the statistics already collected from the data to guide the learning algorithm. This allows us to reduce the number of statistics collected during learning and thus speed up the learning time. We show that our method is capable of learning structure from data more efficiently than traditional approaches. Our technique is of particular importance when the size of the datasets is large or when learning from incomplete data. The basic technique that we introduce is general and can be used to improve learning performance in many settings where sufficient statistics must be computed. In addition, our technique may be useful for alternate search strategies such as branch and bound algorithms.