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UAI
1996
15 years 1 months ago
Context-Specific Independence in Bayesian Networks
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of th...
Craig Boutilier, Nir Friedman, Moisés Golds...
CORR
2010
Springer
162views Education» more  CORR 2010»
14 years 11 months ago
Networked Computing in Wireless Sensor Networks for Structural Health Monitoring
Abstract—This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discu...
Apoorva Jindal, Mingyan Liu
100
Voted
AAAI
2007
15 years 2 months ago
Unscented Message Passing for Arbitrary Continuous Variables in Bayesian Networks
Since Bayesian network (BN) was introduced in the field of artificial intelligence in 1980s, a number of inference algorithms have been developed for probabilistic reasoning. Ho...
Wei Sun, Kuo-Chu Chang
109
Voted
UAI
2000
15 years 1 months ago
Gaussian Process Networks
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Nir Friedman, Iftach Nachman
115
Voted
IJAR
2010
152views more  IJAR 2010»
14 years 11 months ago
Structural-EM for learning PDG models from incomplete data
Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by...
Jens D. Nielsen, Rafael Rumí, Antonio Salme...