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ESSMAC
2003
Springer
12 years 11 months ago
Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models
Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...
Daniel Sbarbaro, Roderick Murray-Smith
IJCV
2008
188views more  IJCV 2008»
12 years 6 months ago
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequen...
Elise Arnaud, Étienne Mémin
NIPS
2008
12 years 7 months ago
Efficient Sampling for Gaussian Process Inference using Control Variables
Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algori...
Michalis Titsias, Neil D. Lawrence, Magnus Rattray
ESSMAC
2003
Springer
12 years 11 months ago
Nonlinear Predictive Control with a Gaussian Process Model
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identiļ¬cation of nonlinear dynamic systems. The Gaussian processes can h...
Jus Kocijan, Roderick Murray-Smith
ICONIP
2009
12 years 3 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
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