Information-Theoretic Limits of Selecting Binary Graphical Models in High Dimensions

The problem of graphical model selection is to estimate the graph structure of a Markov random field given samples from it. We analyze the information-theoretic limitations of the problem of graph selection for binary Markov random fields under high-dimensional scaling, in which the graph size and t...

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Bibliographic Details
Published in:	IEEE transactions on information theory Vol. 58; no. 7; pp. 4117 - 4134
Main Authors:	Santhanam, Narayana P., Wainwright, Martin J.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.07.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Analytical models Applied sciences Coding theory Coding, codes Decoding Exact sciences and technology Graphical models Graphs High dimensional inference Image edge detection Information theory Information, signal and communications theory KL divergence between Ising models Markov analysis Markov processes Markov random fields Probability Random variables sample complexity Sample size Signal and communications theory structure of Ising models Telecommunications and information theory Vectors Error probability Probabilistic approach Necessary and sufficient condition Model selection structure of Ising models Decoding Markov model Markov random fields sample complexity Random field High dimensional inference Pairwise Markov model KL divergence between Ising models Ising model Information theory
ISSN:	0018-9448, 1557-9654
Online Access:	Get full text
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