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Graphical models are a powerful tool for describing patterns of conditional independence, and can also be used to regularize the covariance matrix. Vertices in the graph represent variables, and in the Gaussian setting, edges between vertices are equivalent to non-zero elements in the inverse covariance matrix. Models that can be represented as a decomposable (triangulated) graph are more computationally...
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