A completeness result for d-separation applied to discrete Bayesian networks
is presented and it is shown that in a strong measure-theoretic sense almost
all discrete distributions for a given network structure are faithful; i.e. the
independence facts true of the distribution are all and only those entailed by
the network structure.

介绍一种关于离散贝叶斯网络的 d 分离的完备结果，在强度量论意义下，几乎所有给定网络结构下的离散分布都是忠实的；即分布的独立性事实全部且只包含网络结构蕴涵的部分

贝叶斯网络中的强完备性和忠实度

Strong Completeness and Faithfulness in Bayesian Networks

This paper presents correct algorithms for answering the following two
questions; (i) Does there exist a causal explanation consistent with a set of
background knowledge which explains all of the observed independence facts in a
sample? (ii) Given that there is such a causal explanation what are the causal
relationships common to every such causal explanation?

本文提出了一些正确的算法来回答两个问题: (i) 有没有一个因果解释与一组背景知识一致，解释了样本中观察到的所有独立的事实？ (ii) 假定存在这样一个因果解释，每个这样的因果解释都有哪些共同的因果关系？