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ECML
2005
Springer
15 years 10 months ago
Inducing Hidden Markov Models to Model Long-Term Dependencies
We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models. The induced model is seen as a lumped process of a Markov chain. It is construc...
Jérôme Callut, Pierre Dupont
EPEW
2008
Springer
15 years 6 months ago
State-Aware Performance Analysis with eXtended Stochastic Probes
We define a mechanism for specifying performance queries which combine instantaneous observations of model states and finite sequences of observations of model activities. We reali...
Allan Clark, Stephen Gilmore
CVPR
2008
IEEE
16 years 6 months ago
Trajectory analysis and semantic region modeling using a nonparametric Bayesian model
We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, i...
Xiaogang Wang, Keng Teck Ma, Gee Wah Ng, W. Eric L...
IROS
2009
IEEE
206views Robotics» more  IROS 2009»
15 years 11 months ago
Bayesian reinforcement learning in continuous POMDPs with gaussian processes
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
Patrick Dallaire, Camille Besse, Stéphane R...
AUTOMATICA
2010
96views more  AUTOMATICA 2010»
15 years 4 months ago
Issues in sampling and estimating continuous-time models with stochastic disturbances
: The standard continuous time state space model with stochastic disturbances the mathematical abstraction of continuous time white noise. To work with well defined, discrete time ...
Lennart Ljung, Adrian Wills