摘要
阐述了贝叶斯网络结构学习的内容与方法 ,提出一种基于条件独立性 (CI)测试的启发式算法。从完全潜在图出发 ,融入专家知识和先验常识 ,有效地减少网络结构的搜索空间 ,通过变量之间的CI测试 ,将全连接无向图修剪成最优的潜在图 ,近似于有向无环图的无向版。通过汽车故障诊断实例 ,验证了该算法的可行性与有效性。
This paper discusses the purposes and methods of Bayesian network structure learning, then proposes a new algorithm for this task. Based on a fully connected potential graph, we enter the expert knowledge and prior knowledge in order to reduce the query space of the structures. By using CI (conditional independence) tests, it can be pruned a fully connected potential graph to a best PG, which is expected to approximate the undirected version of the underlying directed graph. The experimental results of fault diagnosis in automobile are provided to illustrate the feasibility and efficiency of the new algorithm.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2004年第4期315-318,共4页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目 (60 175 0 2 2 )
关键词
贝叶斯网络
结构学习
条件独立性
概率推理
图论
Bayesian network
structure learning
conditional independence
probabilistic reasoning
graph theory
