基于模糊约束的贝叶斯网络参数学习

To address the problem of Bayesian networks parameter learning under small datasets, a fuzzy maximum posteriori estimation method is proposed, introducing fuzzy theory into parameter learning. The hyperparameter is determined by using the membership function to measure constraint effectiveness to improve the accuracy of constraint usage for learning. Experiments prove that the proposed method can effectively improve the accuracy of parameter learning. In addition, the proposed parameter learning method is applied to a network security assessment by using common vulnerability scoring system as expert priori parameters and combining vulnerability transfer samples to perform parameter learning. Finally, the node and path security evaluation verifies the effectiveness of the proposed algorithm.