论文标题
分阶性树的可扩展模型选择：平均形成簇和二进制树
Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees
论文作者
论文摘要
已经提出了几种分阶性树木的结构学习算法，贝叶斯网络的不对称扩展。但是，这些要么不会有效地扩展，因为变量所考虑的增加数量，先验限制了模型集，或者它们找不到与现有方法的可比较模型。在这里，我们根据完全有序的Hyperstage定义了替代算法。我们演示了如何用于获得限制模型空间A-posterii的分阶性树的二次缩放结构学习算法。通过比较分析，我们表明，通过平均后验分布提供的顺序，我们可以在计算时间和模型得分中均超过现有方法。这种方法还使我们能够通过扩展模型空间来学习比现有模型选择技术更多的复杂关系，并说明这可以在真实研究中修饰推论。
Several structure-learning algorithms for staged trees, asymmetric extensions of Bayesian networks, have been proposed. However, these either do not scale efficiently as the number of variables considered increases, a priori restrict the set of models, or they do not find comparable models to existing methods. Here, we define an alternative algorithm based on a totally ordered hyperstage. We demonstrate how it can be used to obtain a quadratically-scaling structural learning algorithm for staged trees that restricts the model space a-posteriori. Through comparative analysis, we show that through the ordering provided by the mean posterior distributions, we can outperform existing methods in both computational time and model score. This method also enables us to learn more complex relationships than existing model selection techniques by expanding the model space and illustrates how this can embellish inferences in a real study.
