Order-based structure learning without score equivalence
Order-based structure learning without score equivalence
复制标题
基于顺序的结构学习，无分数等价性
DOI：
10.1093/biomet/asad052
复制
发表时间：
2023
期刊：
影响因子：
2.7
通讯作者：
Zhou, Quan
中科院分区：
文献类型：
--
作者：
Chang, Hyunwoong;Cai, James J;Zhou, Quan
我们提出了一个经验贝叶斯公式的结构学习问题，事先规范假设，所有节点变量具有相同的误差方差，一个假设，以确保识别的基础因果有向无环图。为了便于有效的后验计算，我们近似的后验概率的每一个排序的最佳有向无环图模型，这自然会导致基于顺序的马尔可夫链蒙特卡罗算法。强选择的一致性，我们的模型在高维设置下，允许异质误差方差的条件下证明，我们的采样器的混合行为进行了理论研究。此外，我们提出了一个新的迭代自顶向下算法，它可以快速产生一个近似的解决方案的结构学习问题，并可以用来初始化马尔可夫链蒙特卡罗采样器。我们证明了我们的方法优于其他国家的最先进的算法在各种模拟设置下，并得出结论，该文件与单细胞的实际数据研究说明所提出的方法的实际优势。
We propose an empirical Bayes formulation of the structure learning problem, where the prior specification assumes that all node variables have the same error variance, an assumption known to ensure the identifiability of the underlying causal directed acyclic graph. To facilitate efficient posterior computation, we approximate the posterior probability of each ordering by that of a best directed acyclic graph model, which naturally leads to an order-based Markov chain Monte Carlo algorithm. Strong selection consistency for our model in high-dimensional settings is proved under a condition that allows heterogeneous error variances, and the mixing behaviour of our sampler is theoretically investigated. Furthermore, we propose a new iterative top-down algorithm, which quickly yields an approximate solution to the structure learning problem and can be used to initialize the Markov chain Monte Carlo sampler. We demonstrate that our method outperforms other state-of-the-art algorithms under various simulation settings, and conclude the paper with a single-cell real-data study illustrating practical advantages of the proposed method.