Bayesian Graphical Models for Multivariate Functional Data

Abstract

Graphical models express conditional independence relationships among variables. Although methods for vector-valued data are well established, functional data graphical models remain underdeveloped. We introduce a notion of conditional independence between random functions, and construct a framework for Bayesian inference of undirected, decomposable graphs in the multivariate functional data context. This framework is based on extending Markov distributions and hyper Markov laws from random variables to random processes, providing a principled alternative to naive application of multivariate methods to discretized functional data. Markov properties facilitate the composition of likelihoods and priors according to the decomposition of a graph. Our focus is on Gaussian process graphical models using orthogonal basis expansions. We propose a hyper-inverse-Wishart-process prior for the covariance kernels of the infinite coefficient sequences of the basis expansion, establish existence, uniqueness, strong hyper Markov property, and conjugacy. Stochastic search Markov chain Monte Carlo algorithms are developed for posterior inference, assessed through simulations, and applied to a study of brain activity and alcoholism.

References in corpus (3)

• High-dimensional Ising model selection using ${\ell }_1$-regularized logistic regression
• High-dimensional structure estimation in Ising models: Local separation criterion
• Fast search for Dirichlet process mixture models

Cited by in corpus (5)

• Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model
• Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes
• Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
• Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
• Mean and Covariance Estimation for Discretely Observed High-Dimensional Functional Data: Rates of Convergence and Division of Observational Regimes