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JMLR
2010
137views more  JMLR 2010»
14 years 12 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
175
Voted
JMLR
2010
140views more  JMLR 2010»
14 years 12 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
CVPR
1999
IEEE
16 years 7 months ago
Vision-Based Speaker Detection Using Bayesian Networks
The development of user interfaces based on vision and speech requires the solution of a challenging statistical inference problem: The intentions and actions of multiple individu...
James M. Rehg, Kevin P. Murphy, Paul W. Fieguth
UAI
2008
15 years 6 months ago
Learning Arithmetic Circuits
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
Daniel Lowd, Pedro Domingos
178
Voted
HYBRID
2000
Springer
15 years 8 months ago
A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models
Abstract. Switching linear dynamic systems (SLDS) attempt to describe a complex nonlinear dynamic system with a succession of linear models indexed by a switching variable. Unfortu...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham