摘要： 以贝叶斯概率和贝叶斯网络基本理论为基础，主要研究了贝叶斯网络的结构学习和使用贝叶斯网络进行知识发现和决策支持的方法。利用WILD算法对防洪数据进行了属性离散化，采用K2算法在防洪决策中建立了一种贝叶斯网络模型，并对该模型进行了概率依赖关系描述。说明了贝叶斯网络对数据库进行知识发现和决策支持的有效性。

关键词: 贝叶斯概率, 贝叶斯网络, 结构学习, 知识发现, 决策支持系统

Abstract: The paper mainly studies structure learning of Bayesian networks for knowledge discovery and decision supporting system. Algorithm WILD is used to discrete the continuous attributes in database of the flood. Bayesian network model in the flood decision supporting system is obtained by K2. Explanations of the model are given in the end. It shows that the model is correct and the Bayesian network is a good way in flood decision supporting system.

中图分类号: 

• TP181