EcoSta 2022: Start Registration
View Submission - EcoSta2022
A0985
Title: Functional Bayesian networks Authors:  Fangting Zhou - Texas AM University (United States) [presenting]
Abstract: 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. We develop a novel Bayesian network model for multivariate functional data where the conditional independence and causal structure are represented by a directed acyclic graph. Specifically, we allow the functional objects to deviate from the common Gaussian process assumption, which is key for unique causal structure identification even when the functional data are purely observational and measured with noise. A fully Bayesian framework is designed to infer the functional Bayesian network model with natural uncertainty quantification through posterior summaries. Simulation studies and a real data application with brain electroencephalogram records demonstrate the utility of the proposed model.