基于AHP/D-S证据理论的贝叶斯网络参数学习方法

Bayesian network parameter learning method based on AHP/D-S evidence theory

针对贝叶斯网络构建过程中先验知识的获取问题,将AHP/D-S证据理论引入到贝叶斯网络参数学习中。设计了应用AHP/D-S证据理论整合专家先验知识,综合单调性约束和近等式约束进行参数学习的算法,并进行了仿真案例研究。结果表明:该算法从原理上能够进一步提高贝叶斯网络参数学习的精度,仿真结果也明显优于极大似然估计和无先验信息的最大后验估计的结果,为贝叶斯网络参数学习过程中先验知识的获取提供了一种新的方法。

Aiming at the problem of prior knowledge acquisition in the process of Bayesian network construction,AHP/D-S evidence theory was introduced into Bayesian network parameter learning.An algorithm was proposed for parameter learning by using AHP/D-S evidence theory to integrate expert prior knowledge and integrating monotonic constraints and near-equal constraints.And simulation cases were studied which indicated that applying AHP/D-S evidence theory to integrate expert prior knowledge can improve the accuracy of Bayesian network parameter learning in principle,and the si-mulation results are obviously better than maximum likelihood estimate(MLE)and maximum a prosterior(MAP)without prior information.This offers a new method for acquiring prior knowledge in the Bayesian network parameter learning process.