He, Z.; Xu, R.-J. & Wang, B.-H.
(2014):

*Network reconstruction by stationary distribution data of Markov chains*

based on correlation analysis.

[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
We propose a new method for network reconstruction by the stationary

stribution data of Markov chains on this network. Our method has the merits

at: the data we need are much few than most method and need not defer to the

me order, and we do not need the input data. We define some criterions to

asure the efficacy and the simulation results on several networks, including

mputer-generated networks and real networks, indicate our method works well.

e method consist of two procedures, fist, reconstruct degree sequence,

cond, reconstruct the network(or edges). And we test the efficacy of each

ocedure.

@misc{he2014network,
author = {He, Zhe and Xu, Rui-Jie and Wang, Bing-Hong},
title = {Network reconstruction by stationary distribution data of Markov chains

based on correlation analysis},
year = {2014},
note = {cite arxiv:1410.4120Comment: 4 pages, 3 figures},
url = {http://arxiv.org/abs/1410.4120},
keywords = {chain, markov, network, reconstruction, stationary},
abstract = {We propose a new method for network reconstruction by the stationarydistribution data of Markov chains on this network. Our method has the meritsthat: the data we need are much few than most method and need not defer to thetime order, and we do not need the input data. We define some criterions tomeasure the efficacy and the simulation results on several networks, includingcomputer-generated networks and real networks, indicate our method works well.The method consist of two procedures, fist, reconstruct degree sequence,second, reconstruct the network(or edges). And we test the efficacy of eachprocedure.}
}

%0 = misc
%A = He, Zhe and Xu, Rui-Jie and Wang, Bing-Hong
%B = }
%C =
%D = 2014
%I =
%T = Network reconstruction by stationary distribution data of Markov chains

based on correlation analysis}
%U = http://arxiv.org/abs/1410.4120

Toepfer, M.; Kluegl, P.; Hotho, A. & Puppe, F.
(2011):
Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions.
In: Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation,

[Volltext]
[BibTeX][Endnote]
@inproceedings{toepfer2011segmentation,
author = {Toepfer, Martin and Kluegl, Peter and Hotho, Andreas and Puppe, Frank},
title = {Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions},
booktitle = {Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation},
year = {2011},
url = {http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2011-LWA-SkYp.pdf},
keywords = {2011, chain, conditional, myown, references, segmentation}
}

%0 = inproceedings
%A = Toepfer, Martin and Kluegl, Peter and Hotho, Andreas and Puppe, Frank
%B = Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation
%D = 2011
%T = Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions
%U = http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2011-LWA-SkYp.pdf

Gilks, W. & Spiegelhalter, D. (Hrsg.)
(1996):
Markov chain Monte Carlo in practice.
Erscheinungsjahr/Year: 1996.
Verlag/Publisher: Chapman & Hall/CRC,

[Volltext] [BibTeX]
[Endnote]
@book{gilks1996markov,
author = {Gilks, W.R. and Spiegelhalter, DJ},
title = {Markov chain Monte Carlo in practice},
publisher = {Chapman & Hall/CRC},
year = {1996},
url = {http://scholar.google.de/scholar.bib?q=info:AN5YKWErdFAJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0},
keywords = {carlo, chain, gibbs, learning, markov, mchine, ml, monte}
}

%0 = book
%A = Gilks, W.R. and Spiegelhalter, DJ
%D = 1996
%I = Chapman & Hall/CRC
%T = Markov chain Monte Carlo in practice
%U = http://scholar.google.de/scholar.bib?q=info:AN5YKWErdFAJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0