A BAYESIAN METHOD FOR THE INDUCTION OF PROBABILISTIC NETWORKS FROM DATA
A BAYESIAN METHOD FOR THE INDUCTION OF PROBABILISTIC NETWORKS FROM DATA
复制标题
DOI：
10.1007/bf00994110
复制
发表时间：
1992-10-01
期刊：
影响因子：
7.5
通讯作者：
HERSKOVITS, E
中科院分区：
文献类型：
--
作者：
COOPER, GF;HERSKOVITS, E
提出了一种从数据库中构造概率网络的贝叶斯方法。特别是，我们专注于构建贝叶斯信任网络。潜在的应用包括计算机辅助假设检验、自动科学发现和自动构建概率专家系统。我们将基本方法扩展到处理缺失数据和隐藏(潜在)变量。我们展示了如何通过对多个信念网络的推理进行平均来执行概率推理。给出了从案例数据库中构建信任网络的算法的初步评估结果。最后，我们将本文的方法与以前的工作联系起来，并讨论了有待解决的问题。
This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.