摘要
贝叶斯网络是用来表示变量集合的连续概率分布的图形模式 ,它提供了一种自然地表示因果信息的方法 ,用来发现数据间的潜在关系 .贝叶斯网络的学习也就是要找出一个能够最真实反映现有数据库中各数据变量相互之间的依赖关系的贝叶斯网络模型 ,即根据数据样本 D和先验知识 ζ,找出后验概率 p( sh |D,ζ)最大的贝叶斯网络 S.该文在数学上对贝叶斯网络的学习方法进行了严格的推导 ,用一个实例来说明贝叶斯网络的计算过程 。
Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. It is a natural way to express the causal information, and to discover the hidden patterns among the data . Learning of Bayesian network is to find out a network model that best represents the dependent relationships of the variables in a database, that is, given sample D and prior knowledge ζ, to find a Bayesian network S that fits the maximum posterior probability p(s h|D,ζ). In this paper, the learning process of the network is strictly derived , and a case study is presented to indicate the applications of Bayesian network in data mining.
出处
《软件学报》
EI
CSCD
北大核心
2000年第5期660-666,共7页
Journal of Software
基金
国家CIMS工程研究中心基金! (No.CIMS-JJ.96-001 )资助
关键词
数据挖掘
贝叶斯网络
贝叶斯概率
数据库
Data mining, Bayesian network, Bayesian probability, prior probability, posterior probability.
