High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion

High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion

We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish struct...

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Bibliographic Details
Main Authors: Willsky, Alan S. (Contributor), Tan, Vincent Yan Fu (Contributor), Anandkumar, Animashree (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Massachusetts Institute of Technology. Stochastic Systems Group (Contributor)
Format: Article
Language:English
Published: Association for Computing Machinery (ACM), 2013-07-02T18:40:53Z.
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