论文标题
学习具有多向边缘的线性非高斯图形模型
Learning Linear Non-Gaussian Graphical Models with Multidirected Edges
论文作者
论文摘要
在本文中，我们提出了一种新方法，以了解观察数据的线性非高斯结构方程模型的潜在无环混合图。我们以Wang和Drton提出的算法为基础，我们表明可以通过学习{\ em Multiredirected Edgets}而不仅仅是指向和双向的，可以增强恢复模型的隐藏变量结构。当观察到的两个以上的变量具有隐藏的共同原因时，会出现多向边缘。我们通过查看高阶累积物并利用多行业规则来检测这种隐藏原因的存在。当底层图是具有潜在多导向边缘的无弓形无环混合图时，我们的方法恢复了正确的结构。
In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data. We build on an algorithm proposed by Wang and Drton, and we show that one can augment the hidden variable structure of the recovered model by learning {\em multidirected edges} rather than only directed and bidirected ones. Multidirected edges appear when more than two of the observed variables have a hidden common cause. We detect the presence of such hidden causes by looking at higher order cumulants and exploiting the multi-trek rule. Our method recovers the correct structure when the underlying graph is a bow-free acyclic mixed graph with potential multi-directed edges.
