Functional Bayesian networks for discovering causality from multivariate functional data.
Biometrics
; 79(4): 3279-3293, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37635676
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. 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.
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Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
Tipo de estudo:
Etiology_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article