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» Learning Continuous Time Bayesian Networks
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IJAR
2010
97views more  IJAR 2010»
13 years 6 months ago
Parameter estimation and model selection for mixtures of truncated exponentials
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing ...
Helge Langseth, Thomas D. Nielsen, Rafael Rum&iacu...
EUROMICRO
1998
IEEE
13 years 12 months ago
The World Wide Wait: Where Does the Time Go?
The continuing explosive growth of the web has not been matched by an adequate enhancement of the infrastructure on which it depends. Both consumers and producers are often left f...
Colin Allison, Martin Bramley, Jose Serrano
SUTC
2010
IEEE
13 years 11 months ago
A Log-Ratio Information Measure for Stochastic Sensor Management
—In distributed sensor networks, computational and energy resources are in general limited. Therefore, an intelligent selection of sensors for measurements is of great importance...
Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck
UM
2001
Springer
14 years 4 days ago
Recognizing Time Pressure and Cognitive Load on the Basis of Speech: An Experimental Study
In an experimental environment, we simulated the situation of a user who gives speech input to a system while walking through an airport. The time pressure on the subjects and the ...
Christian A. Müller, Barbara Großmann-H...
IJCNN
2006
IEEE
14 years 1 months ago
A Monte Carlo Sequential Estimation for Point Process Optimum Filtering
— Adaptive filtering is normally utilized to estimate system states or outputs from continuous valued observations, and it is of limited use when the observations are discrete e...
Yiwen Wang 0002, António R. C. Paiva, Jose ...