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Learning Latent Variable Gaussian Graphical Models
Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems.
Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models
In this paper, we propose a general framework for distributed estimation based on a maximum marginal likelihood (MML) approach.
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