基于证据理论/层次分析法的贝叶斯网络建模方法

针对依据专家知识推断贝叶斯网络中条件概率表(CPT)时存在的个体推断信息缺乏完备性和精确性以及整体集成结果缺乏科学性的问题,提出了基于证据理论/层次分析法(DS/AHP)的能够从专家推断信息中提取最优条件概率的方法.首先,通过引入DS/AHP方法中的知识矩阵提出了有利于实现判断对象更直观、判断方式更完善的推断信息提取机制;其次,在此基础上遵循由前至后的推断顺序提出了贝叶斯网络的构建过程;最后,应用传统方法与提出方法对同一贝叶斯网络中的缺失条件概率表进行了推断.数值对比分析表明,所提方法能够在提高计算效率的同时将累计总偏差降低41%,验证了所提方法的科学有效性和应用可行性.

Construction method for Bayesian network based on Dempster-Shafer/analytic hierarchy process

Concerning the problem of lacking completeness and accuracy in the individuals inference information and scientificity in the overall integration results, which exists in the process of inferring Conditional Probability Table (CPT) in Bayesian network according to expert knowledge, this paper presented a method based on the Dempster-Shafer/Analytic Hierarchy Process (DS/AHP) to derive optimal conditional probability from the expert inference information. Firstly, the inferred information extraction mechanism was proposed to make judgment objects more intuitive and judgment modes more perfect by introducing the knowledge matrix of the DS/AHP method. Then, the construction process of Bayesian network was proposed following an inference sequence of "anterior to later". Finally, the traditional method and the presented method were applied to infer the missing conditional probability table in the same Bayesian network. The numerical comparison analyses show that the calculation efficiency can be improved and the accumulative total deviation can be decreased by 41% through the proposed method. Meanwhile, the proposed method is illustrated to be scientific, applicable and feasible.