Necessary conditions for consistent set-based graphical model selection
暂无分享,去创建一个
[1] T. Speed,et al. Gaussian Markov Distributions over Finite Graphs , 1986 .
[2] E. Levina,et al. Joint estimation of multiple graphical models. , 2011, Biometrika.
[3] Elchanan Mossel,et al. Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms , 2007, SIAM J. Comput..
[4] Martin J. Wainwright,et al. Information-Theoretic Limits of Selecting Binary Graphical Models in High Dimensions , 2009, IEEE Transactions on Information Theory.
[5] Vincent Y. F. Tan,et al. Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates , 2010, J. Mach. Learn. Res..
[6] J. Besag. Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .
[7] Peter Elias,et al. List decoding for noisy channels , 1957 .
[8] Andrea Montanari,et al. Which graphical models are difficult to learn? , 2009, NIPS.
[9] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[10] J. Lafferty,et al. High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.
[11] Young-Han Kim,et al. State Amplification , 2008, IEEE Transactions on Information Theory.
[12] Martin J. Wainwright,et al. Information-theoretic bounds on model selection for Gaussian Markov random fields , 2010, 2010 IEEE International Symposium on Information Theory.
[13] Venkatesan Guruswami,et al. List decoding of error correcting codes , 2001 .
[14] Alexandre d'Aspremont,et al. Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .