Learning Probabilistic Networks from Databases

本研究探讨了贝叶斯方法在数据库构造概率网络中的应用。它的主要重点是构建贝叶斯信念网络。主要目标是：(1)开发计算给定案例数据库的贝叶斯信念网络结构概率的方法，(2)在给定案例数据库的情况下确定最可能的信念网络结构，以及(3)通过对多个信念网络的推断进行加权平均来进行概率推断。潜在的应用包括计算机辅助假设检验、自动科学发现和概率专家系统的自动构建。本课程将探讨整合先验知识与数据、处理缺失数据和发现隐藏变量的方法。特别值得关注的是计算效率高的算法的发展。所开发的方法将使用来自几个领域的数据库进行实证评估。//

This research investigates Bayesian methods for constructing probabilistic networks from databases. Its main focus is on constructing Bayesian belief networks. Primary goals are to (1) develop methods for calculating the probability of a Bayesian belief-network structure given a database of cases, (2) identify the most probable belief-network structure given a database of cases, and (3) perform probabilistic inference by taking a weighted average over the inferences of multiple belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Methods will be explored for intergrateing prior knowledge with data, handling missing data, and discovering hidden (latent) variables. Of particular concern is the development of computationally efficient algorithms. The methods developed will be empirically evaluated using databases from several domains. //