论文标题
学习LWF链图：Markov毯子发现方法
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
论文作者
论文摘要
本文在劳动津 - 弗里登伯格（LWF）解释下，在链图（CGS）中的马尔可夫毯子（CGS）中提供了图形表征。该表征与贝叶斯网络的众所周知的表征不同，并概括了它。我们为LWF CGS中的Markov毛毯发现提供了一种新颖的可扩展性和声音算法，并证明了Graw-Shrink算法，IAMB算法，其变体对于LWF CGS中的Markov Blanket Discovery仍然是正确的。我们提供了一个基于忠实的因果关系数据的LWF CGS结构的基于声音且可扩展的约束框架，并在使用本文中的Markov Blanket Discovery算法时证明其正确性。我们提出的算法与最先进的LCD（通过分解链图）算法进行了积极/竞争性的比较，具体取决于用于Markov blanget Discovery的算法。我们提出的算法使广泛的推理/学习问题在计算上可以易于处理，并且更可靠，因为它们利用了地方。
This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs and prove that the Grow-Shrink algorithm, the IAMB algorithm, and its variants are still correct for Markov blanket discovery in LWF CGs under the same assumptions as for Bayesian networks. We provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data and prove its correctness when the Markov blanket discovery algorithms in this paper are used. Our proposed algorithms compare positively/competitively against the state-of-the-art LCD (Learn Chain graphs via Decomposition) algorithm, depending on the algorithm that is used for Markov blanket discovery. Our proposed algorithms make a broad range of inference/learning problems computationally tractable and more reliable because they exploit locality.
