摘要
动态因果图理论是在信度网基础上发展起来的一种不确定推理模型,两者在知识表达上存在一定的差别,但大体结构类似,在一定的条件下可以进行相互转换。从分析信度网与因果图的知识表达方式的异同入手,最后推导出一种将因果图模型转换为信度网模型的算法,主要从构成信度网模型的两大要素———拓朴结构和条件概率表两方面的生成算法进行了推导和阐述。
The Dynamic Causality Diagram methodology is a new probabilistic reasoning model based on Belief Network. To some extent, it is similar to the Belief Network in structure. So that knowledge from one can be transformed into the other on some conditions. To begin with, this paper discusses the similarities and differences between them, and finally presents a transformation algorithm from Dynamic Causality Diagram into Belief Network. The algorithm is composed of two parts: a mapping algorithm of structure and a generating algorithm of conditional probability tables.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2004年第10期33-36,共4页
Journal of Chongqing University
基金
国家自然科学基金资助项目(60373052)
重庆市科技攻关资助项目(5990)
关键词
动态因果图
信度网
不确定性推理
转换算法
dynamic causality diagram
belief network
probabilistic reasoning under uncertainty
transformation algorithm
