论文标题
学习线性非高斯多晶模型
Learning Linear Non-Gaussian Polytree Models
论文作者
论文摘要
在图形因果发现的背景下，我们适应了线性非高斯无环模型（Lingams）的多功能框架，以提出新算法，以有效地学习多丽菌的图形。我们的方法结合了Chow- Liu算法，该算法首先学习了无向树结构，并与新的方案定向边缘。方向方案评估数据生成分布的矩之间的代数关系，并且计算便宜。我们为我们的方法建立了高维的一致性结果，并在数值实验中比较了不同的算法版本。
In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges. The orientation schemes assess algebraic relations among moments of the data-generating distribution and are computationally inexpensive. We establish high-dimensional consistency results for our approach and compare different algorithmic versions in numerical experiments.
