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Volumn 9, Issue , 2008, Pages 485-516

Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data

Author keywords

Binary data; Convex optimization; Gaussian graphical model; Maximum likelihood estimation; Model selection

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; GAUSSIAN DISTRIBUTION; MATHEMATICAL MODELS; PROBLEM SOLVING;

EID: 41549101939     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (1237)

References (20)
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    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
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    • Gradient directed regularization for sparse gaussian concentration graphs, with applications to inference of genetic networks
    • H. Li and J. Gui. Gradient directed regularization for sparse gaussian concentration graphs, with applications to inference of genetic networks. University of Pennsylvania Technical Report, 2005.
    • (2005) University of Pennsylvania Technical Report
    • Li, H.1    Gui, J.2
  • 11
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    • On the convergence of the coordinate descent method for convex differentiable minimization
    • Z. Q. Luo and P. Tseng. On the convergence of the coordinate descent method for convex differentiable minimization. Journal of Optimization Theory and Applications, 72(1):7-35, 1992.
    • (1992) Journal of Optimization Theory and Applications , vol.72 , Issue.1 , pp. 7-35
    • Luo, Z.Q.1    Tseng, P.2
  • 12
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    • Gaussian markov distributions over finite graphs
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    • Log-determinant relaxation for approximate inference in discrete markov random fields
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.