Functional Bayesian networks for discovering causality from multivariate functional data
Functional Bayesian networks for discovering causality from multivariate functional data
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DOI：
10.1111/biom.13922
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发表时间：
2022-10
期刊：
影响因子：
1.9
通讯作者：
Fangting Zhou;Kejun He;Kunbo Wang;Yanxun Xu;Yang Ni
中科院分区：
文献类型：
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作者：
Fangting Zhou;Kejun He;Kunbo Wang;Yanxun Xu;Yang Ni
多元函数数据有着广泛的应用。一个基本任务是理解这些感兴趣的功能对象之间的因果关系。在本文中，我们建立了一种新的贝叶斯网络（BN）模型，其中条件独立性和因果结构由一个有向无环图编码。具体来说，我们允许功能对象偏离高斯过程，这是识别唯一因果结构的关键，即使函数是用噪声测量的。设计了一个全贝叶斯框架，通过后验总结来推断具有自然不确定性量化的功能BN模型。仿真研究和实际数据实例证明了该模型的实用性。
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model.