论文标题
功能性贝叶斯网络，用于从多元功能数据发现因果关系
Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data
论文作者
论文摘要
多元功能数据在广泛的应用中出现。一项基本任务是了解这些功能性的功能对象之间的因果关系，尚未得到充分探讨。在本文中，我们开发了一种新型的贝叶斯网络模型，用于多元功能数据，其中条件独立性和因果结构均由有向的无环图编码。具体而言，我们允许功能对象偏离高斯过程，高斯流程是由大多数现有功能数据分析模型所采用的。即使用噪声测量功能，也更合理的非高斯假设是独特因果结构识别的关键。完全贝叶斯框架旨在通过后摘要来推断具有自然不确定性定量的功能性贝叶斯网络模型。模拟研究和实际数据示例用于证明所提出模型的实际实用性。
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, 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 process, which is adopted by most existing functional data analysis models. The more reasonable non-Gaussian assumption is the key for unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional Bayesian network model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples are used to demonstrate the practical utility of the proposed model.
