Figure 3.

(a) Nodes 1, 2, and 3 are marginally correlated and they are driven by a common source (node 4). Nodes 1, 2 and 3 are conditionally independent to each other after factoring out the common source. (b) Graphical LASSO promotes the sparsity of inverse covariance matrix (ICOV) and shrinks small entries to zero first. X-axis: regularization parameter λ, y-axis: regularized inverse covariance estimation. (c) Another example of regularized ICOV estimation using graphical LASSO and larger regularization parameter λ produces sparser Θ estimation (i.e., fewer non-zero entries).