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NIPS
2008
13 years 9 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
CORR
2010
Springer
102views Education» more  CORR 2010»
13 years 7 months ago
Error Analysis of Approximated PCRLBs for Nonlinear Dynamics
In practical nonlinear filtering, the assessment of achievable filtering performance is important. In this paper, we focus on the problem of how to efficiently approximate the post...
Ming Lei, Pierre Del Moral, Christophe Baehr
ICCV
2009
IEEE
13 years 5 months ago
Component analysis approach to estimation of tissue intensity distributions of 3D images
Many segmentation problems in medical imaging rely on accurate modeling and estimation of tissue intensity probability density functions. Gaussian mixture modeling, currently the ...
Arridhana Ciptadi, Cheng Chen, Vitali Zagorodnov
JMLR
2010
132views more  JMLR 2010»
13 years 2 months ago
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mu...
ICMLA
2010
13 years 5 months ago
A Probabilistic Graphical Model of Quantum Systems
Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomp...
Chen-Hsiang Yeang