Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data
从多元函数型数据发现因果关系的函数型贝叶斯网络
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, which has not yet been fully explored. In this article, we develop a novel Bayesian network model for multivariate functional data where the conditional independence and causal structure are both encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian…