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- W2129697260 abstract "The problem of graphical model selection is to correctly estimate the graph structure of a Markov random field given samples from the underlying distribution. We analyze the information-theoretic limitations of this problem under high-dimensional scaling, in which the graph size p and the number of edges k (or the maximum degree d) are allowed to increase to infinity as a function of the sample size n. For pairwise binary Markov random fields, we derive both necessary and sufficient conditions on the scaling of the triplet (n, p, k) (or the triplet (n, p, d)) for asympotically reliable reocovery of the graph structure." @default.
- W2129697260 created "2016-06-24" @default.
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- W2129697260 date "2008-07-01" @default.
- W2129697260 modified "2023-09-23" @default.
- W2129697260 title "Information-theoretic limits of graphical model selection in high dimensions" @default.
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- W2129697260 doi "https://doi.org/10.1109/isit.2008.4595367" @default.
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