Learning graph structures in discrete Markov random fields

Rui Wu, R. Srikant, Jian Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 Erdo″s-Rényi random graph G ∼ G(p, c/p).

Original languageEnglish (US)
Title of host publication2012 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2012
Pages214-219
Number of pages6
DOIs
StatePublished - May 24 2012
Event2012 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2012 - Orlando, FL, United States
Duration: Mar 25 2012Mar 30 2012

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X

Other

Other2012 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2012
CountryUnited States
CityOrlando, FL
Period3/25/123/30/12

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Learning graph structures in discrete Markov random fields'. Together they form a unique fingerprint.

  • Cite this

    Wu, R., Srikant, R., & Ni, J. (2012). Learning graph structures in discrete Markov random fields. In 2012 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2012 (pp. 214-219). [6193494] (Proceedings - IEEE INFOCOM). https://doi.org/10.1109/INFCOMW.2012.6193494