Cooperative learning of Bayesian network structure based on PG algorithms

Bayesian network (BN) has become an important and powerful method for representing and reasoning with uncertainty, and has been widely used in artificial intelligence and knowledge engineering. In This work we give an introduction about Bayesian networks, and discuss the related work on learning Bayesian networks. Then we present an efficient algorithm for cooperative learning of BN structure that can combine prior knowledge with the given database. And then we give a study case in business intelligence to demonstrate the feasibility of the algorithm. Eventually, we conclude with some discussion of the future work.