Bayesian inference of high-dimensional, cluster-structured ordinary differential equation models with applications to brain connectivity studies

DOI10.1214/17-AOAS1021zbMath1391.62262WikidataQ115240839 ScholiaQ115240839MaRDI QIDQ2404447

Yinge Sun, Dana Boatman-Reich, Qiannan Yin, Ting-Ting Zhang, Brian S. Caffo

Publication date: 18 September 2017

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1500537727

zbMATH Keywords

Bayesian inferencecluster structureODE modelsdirectional brain networksnetwork edge selection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (6)

A Bayesian State-Space Approach to Mapping Directional Brain Networks ⋮ A joint estimation approach to sparse additive ordinary differential equations ⋮ Adaptive Semiparametric Bayesian Differential Equations Via Sequential Monte Carlo ⋮ Kernel Ordinary Differential Equations ⋮ A variational Bayesian approach to identifying whole-brain directed networks with fMRI data ⋮ Bayesian inference of a directional brain network model for intracranial EEG data

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