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UAI
2003
13 years 11 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
EUROPAR
2010
Springer
13 years 11 months ago
cTrust: Trust Aggregation in Cyclic Mobile Ad Hoc Networks
In a Cyclic Mobile Ad Hoc Network (CMANET) where nodes move cyclically, we formulate trust management problems and propose the cTrust scheme to handle trust establishment and aggre...
Huanyu Zhao, Xin Yang, Xiaolin Li
GECCO
2008
Springer
171views Optimization» more  GECCO 2008»
13 years 11 months ago
An EDA based on local markov property and gibbs sampling
The key ideas behind most of the recently proposed Markov networks based EDAs were to factorise the joint probability distribution in terms of the cliques in the undirected graph....
Siddhartha Shakya, Roberto Santana
DATAMINE
2010
175views more  DATAMINE 2010»
13 years 10 months ago
Extracting influential nodes on a social network for information diffusion
We address the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models...
Masahiro Kimura, Kazumi Saito, Ryohei Nakano, Hiro...
IM
2007
13 years 9 months ago
Ranking Websites: A Probabilistic View
In this paper we suggest evaluating the importance of a website with the mean frequency of visiting the website for the Markov chain on the Internet Graph describing random surfi...
Ying Bao, Guang Feng, Tie-Yan Liu, Zhiming Ma, Yin...