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» Explaining inferences in Bayesian networks
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IJON
2010
138views more  IJON 2010»
13 years 6 months ago
A dynamic Bayesian network to represent discrete duration models
Originally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in var...
Roland Donat, Philippe Leray, Laurent Bouillaut, P...
CJ
2010
131views more  CJ 2010»
13 years 5 months ago
Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks
an be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The...
Duncan Gillies, David Thornley, Chatschik Bisdikia...
JMLR
2010
137views more  JMLR 2010»
13 years 2 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
JMLR
2010
140views more  JMLR 2010»
13 years 2 months ago
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
NIPS
2004
13 years 9 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Pradeep Shenoy, Rajesh P. N. Rao