论文标题
简单的树木的结构学习
Structural Learning of Simple Staged Trees
论文作者
论文摘要
贝叶斯网络忠实地代表了随机向量组件之间存在的对称条件独立性。分阶性树是用于分类随机向量的贝叶斯网络的扩展，其图代表了通过顶点着色的非对称条件独立性。但是，由于它们基于样品空间的树表示，因此随着变量数量的增加，基础图变得混乱且难以可视化。在这里，我们介绍了简单分阶段的第一个结构学习算法，并在基础树上融合了紧凑的聚结，可以轻松阅读非对称独立性。我们表明，数据学习的简单分阶段通常在模型拟合中的贝叶斯网络通常超过贝叶斯网络，并说明了如何使用合并图来识别非对称条件独立性。
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. However, since they are based on a tree representation of the sample space, the underlying graph becomes cluttered and difficult to visualize as the number of variables increases. Here we introduce the first structural learning algorithms for the class of simple staged trees, entertaining a compact coalescence of the underlying tree from which non-symmetric independences can be easily read. We show that data-learned simple staged trees often outperform Bayesian networks in model fit and illustrate how the coalesced graph is used to identify non-symmetric conditional independences.
