学习包含最优弦图
Learning Inclusion-Optimal Chordal Graphs
摘要：
弦图可用于编码依赖模型，这些依赖模型可由有向无环图和无向图表示。本文讨论了一个非常简单和有效的算法来学习弦结构的概率模型从数据。该算法是一个贪婪的爬山搜索算法，使用包含边界附近的弦图。在限制的大样本量和适当的假设下的评分标准，我们证明了该算法将找到一个结构，是包容性最优的数据生成分布的依赖模型时，可以完全由无向图表示。模拟数据集上的算法进行评估。
英文摘要：
Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses the inclusion boundary neighborhood over chordal graphs. In the limit of a large sample size and under appropriate hypotheses on the scoring criterion, we prove that the algorithm will find a structure that is inclusion-optimal when the dependency model of the data-generating distribution can be represented exactly by an undirected graph. The algorithm is evaluated on simulated datasets.
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