High-dimensional Gaussian graphical model selection: walk summability and local separation criterion

@article{Anandkumar2012HighdimensionalGG,
  title={High-dimensional Gaussian graphical model selection: walk summability and local separation criterion},
  author={Anima Anandkumar and Vincent Y. F. Tan and Furong Huang and Alan S. Willsky},
  journal={Journal of Machine Learning Research},
  year={2012},
  volume={13},
  pages={2293-2337}
}
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 structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n = Ω(J min log p), where p is the number of variables and Jmin is the minimum (absolute) edge potential of… CONTINUE READING
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