IDENTIFYING INDEPENDENCE IN BAYESIAN NETWORKS
IDENTIFYING INDEPENDENCE IN BAYESIAN NETWORKS
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DOI：
10.1002/net.3230200504
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发表时间：
1990-08-01
期刊：
影响因子：
2.1
通讯作者：
PEARL, J
中科院分区：
文献类型：
--
作者：
GEIGER, D;VERMA, T;PEARL, J
贝叶斯网络的一个重要特征是，它们促进了关于域中独立性的信息的显式编码，这些信息对于有效的推理是不可或缺的。本文描述了从网络拓扑逻辑上得出的所有独立性断言，并开发了一个识别这些断言的线性时间算法。该算法的正确性是基于一个图形标准的合理性，称为d-分离，其最优性源于d-分离的完整性。定义了一个增强版本的d-分离，称为D-分离，将算法扩展到编码函数依赖的网络。最后，该算法被证明适用于广泛的一类非概率独立性。
An important feature of Bayesian networks is that they facilitate explicit encoding of information about independencies in the domain, information that is indispensable for efficient inferencing. This article characterizes all independence assertions that logically follow from the topology of a network and develops a linear time algorithm that identifies these assertions. The algorithm's correctness is based on the soundness of a graphical criterion, calledd‐separation, and its optimality stems from the completeness ofd‐separation. An enhanced version ofd‐separation, calledD‐separation, is defined, extending the algorithm to networks that encode functional dependencies. Finally, the algorithm is shown to work for a broad class of nonprobabilistic independencies.