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» Feature Correspondence: A Markov Chain Monte Carlo Approach
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JMLR
2011
148views more  JMLR 2011»
13 years 2 months ago
Bayesian Generalized Kernel Mixed Models
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...
Zhihua Zhang, Guang Dai, Michael I. Jordan
ICIP
2009
IEEE
14 years 8 months ago
Lidar Waveform Modeling Using A Marked Point Process
Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate...
ICASSP
2008
IEEE
14 years 1 months ago
Image inpainting with a wavelet domain Hidden Markov tree model
We present a novel technique for image inpainting, the problem of filling-in missing image parts. Image inpainting is ill-posed and we adopt a probabilistic model-based approach ...
George Papandreou, Petros Maragos, Anil Kokaram
CVPR
2007
IEEE
14 years 9 months ago
Metropolis-Hasting techniques for finite-element-based registration
In this paper, we focus on the design of Markov Chain Monte Carlo techniques in a statistical registration framework based on finite element basis (FE). Due to the use of FE basis...
Adeline M. M. Samson, Frédéric J. P....
ICML
2007
IEEE
14 years 8 months ago
Most likely heteroscedastic Gaussian process regression
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
Kristian Kersting, Christian Plagemann, Patrick Pf...