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» Dynamic Histograms: Capturing Evolving Data Sets
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RTS
2008
74views more  RTS 2008»
13 years 7 months ago
Quantifying and suppressing the measurement disturbance in feedback controlled real-time systems
In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the dynamics of the system. In general, this property cannot be app...
Mehdi Amirijoo, Jörgen Hansson, Svante Gunnar...
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
ICAC
2009
IEEE
14 years 2 months ago
Applying genetic algorithms to decision making in autonomic computing systems
Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means...
Andres J. Ramirez, David B. Knoester, Betty H. C. ...
IJSNET
2007
155views more  IJSNET 2007»
13 years 7 months ago
Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks
: A Bayesian distributed online change detection algorithm is proposed for monitoring a dynamical system by a wireless sensor network. The proposed solution relies on modelling the...
Hichem Snoussi, Cédric Richard
NIPS
2001
13 years 9 months ago
The Infinite Hidden Markov Model
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integ...
Matthew J. Beal, Zoubin Ghahramani, Carl Edward Ra...