based on correlation analysis JO - PB - C1 - PY - 2014/ VL - IS - SP - EP - UR - http://arxiv.org/abs/1410.4120 DO - KW - chain KW - markov KW - network KW - reconstruction KW - stationary L1 - N1 - Network reconstruction by stationary distribution data of Markov chains
based on correlation analysis N1 - AB - We propose a new method for network reconstruction by the stationary
distribution data of Markov chains on this network. Our method has the merits
that: the data we need are much few than most method and need not defer to the
time order, and we do not need the input data. We define some criterions to
measure the efficacy and the simulation results on several networks, including
computer-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 each
procedure. ER - TY - CONF AU - Toepfer, Martin AU - Kluegl, Peter AU - Hotho, Andreas AU - Puppe, Frank A2 - T1 - Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions T2 - Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation PB - C1 - PY - 2011/ CY - VL - IS - SP - EP - UR - http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2011-LWA-SkYp.pdf DO - KW - 2011 KW - chain KW - conditional KW - myown KW - references KW - segmentation L1 - SN - N1 - N1 - AB - ER - TY - BOOK AU - Gilks, W.R. AU - Spiegelhalter, DJ A2 - T1 - Markov chain Monte Carlo in practice PB - Chapman & Hall/CRC C1 - PY - 1996/ VL - IS - SP - EP - UR - http://scholar.google.de/scholar.bib?q=info:AN5YKWErdFAJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0 DO - KW - carlo KW - chain KW - gibbs KW - learning KW - markov KW - mchine KW - ml KW - monte L1 - SN - N1 - N1 - AB - ER -