Bayesian Regularization for Functional Graphical Models with Applications to Neuroimaging. (arXiv:2110.05575v2 [stat.ME] UPDATED)

Graphical models, used to express conditional dependence between random
variables observed at various nodes, are used extensively in many fields such
as genetics, neuroscience, and social network analysis. While most current
statistical methods for estimating graphical models focus on scalar data, there
is interest in estimating analogous dependence structures when the data
observed at each node are functional, such as signals or images. In this paper,
we propose a fully Bayesian regularization scheme for estimating functional
graphical models. We first …

applications arxiv bayesian neuroimaging regularization