Distributed learning of Gaussian graphical models via marginal likelihoods

Keyphrases

• Gaussian Graphical Model 100%
• Distributed Learning 100%
• Local Estimates 100%
• Marginal Likelihood 100%
• Inverse Covariance Matrix 50%
• Numerical Experiments 50%
• Non-convexity 50%
• Tween 50%
• Tion 50%
• Maximum Likelihood 50%
• Convex Relaxation 50%
• Local Neighborhood 50%
• Distributed Estimation 50%
• Closing the Gap 50%
• Concentration Matrix 50%
• Iterative Message Passing 50%
• One-hop 50%
• Centralized Estimation 50%
• Global Inference 50%
• Two-hop 50%

Mathematics

• Gaussian Distribution 100%
• Marginal Likelihood 100%
• Marginals 100%
• Graphical Model 100%
• Matrix (Mathematics) 33%
• Inverse Covariance Matrix 33%
• Real-World Data 33%
• Numerical Experiment 33%

Computer Science

• Graphical Model 100%
• Distributed Learning 100%
• Marginal Likelihood 100%
• Collected Data 50%
• Message Passing 50%
• Convex Relaxation 50%
• Covariance Matrix 50%