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» Network Inference from Co-Occurrences
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ML
2006
ACM
131views Machine Learning» more  ML 2006»
15 years 3 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
155
Voted
PE
2002
Springer
124views Optimization» more  PE 2002»
15 years 3 months ago
Continuous-time hidden Markov models for network performance evaluation
In this paper, we study the use of continuous-time hidden Markov models (CT-HMMs) for network protocol and application performance evaluation. We develop an algorithm to infer the...
Wei Wei, Bing Wang, Donald F. Towsley
117
Voted
CORR
2010
Springer
127views Education» more  CORR 2010»
15 years 2 months ago
Learning Networks of Stochastic Differential Equations
We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the d...
José Bento, Morteza Ibrahimi, Andrea Montan...
WOWMOM
2005
ACM
115views Multimedia» more  WOWMOM 2005»
15 years 9 months ago
Precise Distributed Localization Algorithms for Wireless Networks
We propose in this paper reliable and precise distributed localization algorithms for wireless networks: iterative multidimensional scaling (IT-MDS) and simulated annealing multid...
Saad Biaz, Yiming Ji
187
Voted
IDA
2005
Springer
15 years 9 months ago
Combining Bayesian Networks with Higher-Order Data Representations
Abstract. This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the...
Elias Gyftodimos, Peter A. Flach