Information-theoretic bounds on model selection for Gaussian Markov random fields

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2010, Pages 1373-1377

  Wang, Wei ^a   Wainwright, Martin J ^a,b   Ramchandran, Kannan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FROBENIUS NORM; GAUSSIAN MARKOV RANDOM FIELD; GAUSSIANS; GRAPH SIZES; GRAPH STRUCTURES; GRAPHICAL MODEL; HIGH-DIMENSIONAL; INFORMATION THEORETIC BOUNDS; INVERSE COVARIANCE; MARKOV RANDOM FIELDS; MATRIX; MODEL SELECTION; NODE DEGREE; OBSERVED SAMPLES; POLYNOMIAL-TIME ALGORITHMS; SAMPLE SIZES; SUFFICIENT CONDITIONS; THEORETIC LIMITATIONS; UNDERLYING GRAPHS;

GAUSSIAN DISTRIBUTION; GRAPHIC METHODS; IMAGE SEGMENTATION; INFORMATION THEORY;

COVARIANCE MATRIX;

EID: 77955688227     PISSN: 21578103     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2010.5513573     Document Type: Conference Paper

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