Learning Probabilistic Networks from Databases

本研究探讨从数据库中构建概率网络的贝叶斯方法。它的主要关注点是构建贝叶斯信念网络。主要目标是(1)开发用于计算给定案例数据库的贝叶斯信任网络结构的概率的方法，(2)在给定案例数据库的情况下识别最可能的信任网络结构，以及(3)通过对多个信任网络的推理进行加权平均来执行概率推理。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。将探索将先验知识与数据整合、处理缺失数据和发现隐藏(潜在)变量的方法。尤其值得关注的是计算效率算法的发展。开发的方法将使用几个领域的数据库进行经验评估。//

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. //