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» Exploiting Data Missingness in Bayesian Network Modeling
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BMCBI
2010
178views more  BMCBI 2010»
15 years 3 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
ICDCS
2006
IEEE
15 years 9 months ago
Scalable and Robust Aggregation Techniques for Extracting Statistical Information in Sensor Networks
Wireless sensor networks have stringent constraints on system resources and data aggregation techniques are critically important. However, accurate data aggregation is difficult ...
Hongbo Jiang, Shudong Jin
MOBIHOC
2009
ACM
16 years 3 months ago
Multicasting in delay tolerant networks: a social network perspective
Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly impair the effectiveness of data dissemination. Although social-based approaches can be used ...
Wei Gao, Qinghua Li, Bo Zhao, Guohong Cao
IJCNN
2000
IEEE
15 years 7 months ago
Bi-Causal Recurrent Cascade Correlation
Recurrent neural networks fail to deal with prediction tasks which do not satisfy the causality assumption. We propose to exploit bi-causality to extend the Recurrent Cascade Corr...
Alessio Micheli, Diego Sona, Alessandro Sperduti
IJUFKS
2000
111views more  IJUFKS 2000»
15 years 2 months ago
A Factorized Representation of Independence of Causal Influence and Lazy Propagation
Theefficiency of algorithmsfor probabilistic inference in Bayesian networks can be improvedby exploiting independenceof causal influence. Thefactorized representation of independe...
Anders L. Madsen, Bruce D'Ambrosio