An Expectation Conditional Maximization approach for Gaussian graphical models.
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ABSTRACT:
SUBMITTER: Li ZR
PROVIDER: S-EPMC7540244 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
An Expectation Conditional Maximization approach for Gaussian graphical models.
Li Zehang Richard ZR McCormick Tyler H TH
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20190619 4
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian copula graphical models using an Expectation Conditional Maximization (ECM) algorithm, extendi ...[more]
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