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96
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JETAI
1998
110views more  JETAI 1998»
15 years 7 days ago
Independency relationships and learning algorithms for singly connected networks
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
Luis M. de Campos
ICC
2007
IEEE
125views Communications» more  ICC 2007»
15 years 7 months ago
Scalable Fault Diagnosis in IP Networks using Graphical Models: A Variational Inference Approach
In this paper we investigate the fault diagnosis problem in IP networks. We provide a lower bound on the average number of probes per edge using variational inference technique pro...
Rajesh Narasimha, Souvik Dihidar, Chuanyi Ji, Stev...
97
Voted
IPMU
2010
Springer
15 years 4 months ago
Credal Sets Approximation by Lower Probabilities: Application to Credal Networks
Abstract. Credal sets are closed convex sets of probability mass functions. The lower probabilities specified by a credal set for each element of the power set can be used as cons...
Alessandro Antonucci, Fabio Cuzzolin
120
Voted
AAAI
2008
15 years 2 months ago
Hybrid Markov Logic Networks
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
Jue Wang, Pedro Domingos
126
Voted
METMBS
2003
255views Mathematics» more  METMBS 2003»
15 years 1 months ago
Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery
Causal Probabilistic Networks (CPNs), (a.k.a. Bayesian Networks, or Belief Networks) are well-established representations in biomedical applications such as decision support system...
Constantin F. Aliferis, Ioannis Tsamardinos, Alexa...