TY - GEN
T1 - Learning graph structures in discrete Markov random fields
AU - Wu, Rui
AU - Srikant, R.
AU - Ni, Jian
PY - 2012
Y1 - 2012
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=84862078784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862078784&partnerID=8YFLogxK
U2 - 10.1109/INFCOMW.2012.6193494
DO - 10.1109/INFCOMW.2012.6193494
M3 - Conference contribution
AN - SCOPUS:84862078784
SN - 9781467310178
T3 - Proceedings - IEEE INFOCOM
SP - 214
EP - 219
BT - 2012 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2012
T2 - 2012 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2012
Y2 - 25 March 2012 through 30 March 2012
ER -