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Learning graph structures in discrete Markov random fields

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3 Author(s)
Rui Wu ; Dept. of ECE & CSL, Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Srikant, R. ; Jian Ni

We present a general algorithm for learning the structure of discrete Markov random fields from i.i.d. samples. The algorithm either achieves the same computational complexity or lowers the computational complexity of earlier algorithms for several cases, and provides a new low-computational complexity algorithm for the case of Ising models where the underlying graph is the Erdos-Rényi random graph G ~ G(p, c/p).

Published in:

Computer Communications Workshops (INFOCOM WKSHPS), 2012 IEEE Conference on

Date of Conference:

25-30 March 2012

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