Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE

Raskutti, Garvesh, Yu, Bin, Wainwright, Martin J., Ravikumar, Pradeep K.

Dec-31-2009–Neural Information Processing Systems

We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d.

graph, matrix, probability, (15 more...)

Neural Information Processing Systems

Dec-31-2009

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Country:
- North America > United States
  - California > Alameda County
    - Hayward (0.04)
    - Berkeley (0.04)
- Europe > United Kingdom
  - England
    - Cambridgeshire > Cambridge (0.14)
    - Oxfordshire > Oxford (0.04)

Technology:
- Information Technology > Artificial Intelligence > Machine Learning
  - Statistical Learning (1.00)
  - Learning Graphical Models > Undirected Networks
    - Markov Models (0.34)

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Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath$\ell_1$-regularized MLE

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