关于链图的分离准则和恢复算法
On Separation Criterion and Recovery Algorithm for Chain Graphs
摘要：
链图给出了马尔可夫网络和贝叶斯网络的自然统一观点，并扩大了图形模型描述条件独立结构的潜力。本文介绍了一种直接的图分离准则，称为c-分离，它推广了贝叶斯网络的d-分离准则。它等价于链图的经典道德化准则，并且在某种意义上是完全的，即对于每个链图，存在一个概率分布，该概率分布满足可由c-分离准则从链图导出的精确条件独立性。每一类马尔可夫等价链图都可以用一个自然表示唯一地描述，称为最大链图。提出了一种基于由未知链图导出的（条件）依赖模型的最大链图恢复算法。
英文摘要：
Chain graphs give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structures. In the paper a direct graphical separation criterion for chain graphs, called c-separation, which generalizes the d-separation criterion for Bayesian networks is introduced (recalled). It is equivalent to the classic moralization criterion for chain graphs and complete in sense that for every chain graph there exists a probability distribution satisfying exactly conditional independencies derivable from the chain graph by the c-separation criterion. Every class of Markov equivalent chain graphs can be uniquely described by a natural representative, called the largest chain graph. A recovery algorithm, which on basis of the (conditional) dependency model induced by an unknown chain graph finds the corresponding largest chain graph, is presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
