论文标题
稀疏的树木的高效结构学习
Highly Efficient Structural Learning of Sparse Staged Trees
论文作者
论文摘要
已经定义了几种分期树模型的结构学习算法，这是贝叶斯网络的不对称扩展。但是，随着变量考虑的增加数量，它们并不能有效地扩展。在这里，我们介绍了第一个针对分阶性树的可扩展结构学习算法，该算法在模型的一个空间上进行了搜索，在这些模型的空间中，只能施加少量的依赖项。一项仿真研究以及现实世界的应用说明了我们的日常工作以及此类数据学习的分阶段的实际使用。
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first scalable structural learning algorithm for staged trees, which searches over a space of models where only a small number of dependencies can be imposed. A simulation study as well as a real-world application illustrate our routines and the practical use of such data-learned staged trees.
