Covariance Estimation in Decomposable Gaussian Graphical Models
Covariance Estimation in Decomposable Gaussian Graphical Models
Graphical models are a framework for representing and exploiting prior conditional independence structures within distributions using graphs. In the Gaussian case, these models are directly related to the sparsity of the inverse covariance (concentration) matrix and allow for improved covariance estimation with lower computational complexity. We consider concentration estimation with …