摘要
通过示例给出了贝叶斯网络的构造方法 ,概括了贝叶斯网络的特点及贝叶斯网络学习的内容与过程 ,同时给出了离散型贝叶斯网络的预测公式 .贝叶斯网络学习主要有三个基本环节 ,其一是确定变量集和变量域 ;其二是确定贝叶斯网络结构 ;其三是确定局部概率分布 .贝叶斯网络是描述变量之间定性与定量依赖关系的图形模式 ,是进行数据联合分析与预测的有力工具 .
In this paper the method of constructing a Bayesian network is introduced and it is summarized what is the characteristic of Bayesian network and the content and process of learning Bayesian network,meanwhile the forecasting formula is introduced on discrete Bayesian network.There are three basic steps in learning Bayesian network,first is to determine the set and domain of variables;second is to determine the structure of network;third is to determine the local probabilistic distribution.Bayesian network is the graphical model of encoding the assertions of qualitative and quantitative relationships between variables and is a very useful tool for joint analysis and forecasting.
出处
《东北师大学报（自然科学版）》
CAS
CSCD
北大核心
2002年第1期9-14,共6页
Journal of Northeast Normal University(Natural Science Edition)
基金
科学技术部国家软科学研究项目 (Z990 1 5 )
