On joint estimation of Gaussian graphical models for spatial and temporal data.

ABSTRACT: In this article, we first propose a Bayesian neighborhood selection method to estimate Gaussian Graphical Models (GGMs). We show the graph selection consistency of this method in the sense that the posterior probability of the true model converges to one. When there are multiple groups of data available, instead of estimating the networks independently for each group, joint estimation of the networks may utilize the shared information among groups and lead to improved estimation for each individual network. Our method is extended to jointly estimate GGMs in multiple groups of data with complex structures, including spatial data, temporal data, and data with both spatial and temporal structures. Markov random field (MRF) models are used to efficiently incorporate the complex data structures

SUBMITTER: Lin Z 

PROVIDER: S-EPMC5515703 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature