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A generalized Gaussian process model (GGPM) is a unifying framework that encompasses many existing Gaussian process (GP) models, such as GP regression, classification, and counting.
A queuing model with state dependent service rates
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Non-Gaussian state¡ªspace modeling of nonstationary time series
Genshiro Kitagawa · 1987
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Computational methods using a Bayesian hierarchical generalized linear model
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Generalized linear models
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Multivariate statistical modelling based on generalized linear models , volume 2
Ludwig Fahrmeir, Gerhard Tutz, and Wolfgang Hennevogl · 1994
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A new perspective on priors for generalized linear models
Edward J. Bedrick, Ronald Christensen, and Wesley Johnson · 1996
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Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
Radford M. Neal · 1997
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Model-based geostatistics
Peter J. Diggle, JA Tawn, and RA Moyeed · 1998
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Bayesian classification with Gaussian processes
Christopher K. I. Williams and David Barber · 1998
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Adaptive Bayesian regression splines in semiparametric generalized linear models
Clemens Biller · 2000
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Variational Gaussian process classifiers
Mark Gibbs and David J. C. Mackay · 2000
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The generalized Bayesian committee machine
Volker Tresp · 2000
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T.F. Cootes, G.J. Edwards, and C.J. Taylor · 2001
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Richard O. Duda, Peter E. Hart, and David G. Stork · 2001
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A family of algorithms for approximate Bayesian inference
Thomas P. Minka · 2001
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Nonstationary covariance functions for Gaussian process regression
Christopher J. Paciorek and Mark J. Schervish · 2004
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Gaussian processes for machine learning
Matthias Seeger · 2004
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Learning depth from stereo
F Sinz, Q Candela, GH Bakir, CE Rasmussen, and M Franz · 2004
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Warped Gaussian processes
Edward Snelson, Carl Edward Rasmussen, and Zoubin Ghahramani · 2004
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Gaussian processes for ordinal regression
Wei Chu and Zoubin Ghahramani · 2005
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Assessing approximate inference for binary Gaussian process classification
Malte Kuss and Carl Edward Rasmussen · 2005
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A useful distribution for fitting discrete data: revival of the conway-maxwell-poisson distribution
Galit Shmueli, Thomas P Minka, Joseph B Kadane, Sharad Borle, and Peter Boatwright · 2005
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Semiparametric latent factor models
Yee Whye Teh, Matthias Seeger, and M I Jordan · 2005
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Priors for people tracking from small training sets
Raquel Urtasun, David J Fleet, Aaron Hertzmann, and Pascal Fua · 2005
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Variational Bayesian multinomial probit regression with Gaussian process priors
Mark Girolami and Simon Rogers · 2006
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Bayesian Gaussian process classification with the EM-EP algorithm
Hyun-Chul Kim and Zoubin Ghahramani · 2006
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Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning
Malte Kuss · 2006
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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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A switched Gaussian process for estimating disparity and segmentation in binocular stereo
Oliver Williams · 2006
Selection and context for action recognition
Dong Han, Liefeng Bo, and Cristian Sminchisescu · 2009
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Modelling multi-object activity by Gaussian processes
Chen Change Loy, Tao Xiang, and Shaogang Gong · 2009
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The variational Gaussian approximation revisited
Opper Manfred and Cédric Archambeau · 2009
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Convex variational Bayesian inference for large scale generalized linear models
Hannes Nickisch and Matthias W. Seeger · 2009
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Gaussian process regression with Student-t likelihood
Jarno Vanhatalo, Pasi Jylänki, and Aki Vehtari · 2009
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Nonrigid shape recovery by Gaussian process regression
Jianke Zhu, Steven CH Hoi, and Michael R Lyu · 2009
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Generalised kernel machines
Gavin C. Cawley, Gareth J. Janacek, and Nicola LC Talbot · 2007
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On Bayesian analysis of generalized linear models: A new perspective
Sourish Das and Dipak K. Dey · 2007
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Gaussian beam processes: A nonparametric Bayesian measurement model for range finders
Christian Plagemann, Kristian Kersting, Patrick Pfaff, and Wolfram Burgard · 2007
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Tracking and classifying of human motions with Gaussian process annealed particle filter
Leonid Raskin, Michael Rudzsky, and Ehud Rivlin · 2007
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Bayesian inference for sparse generalized linear models
Matthias Seeger, Sebastian Gerwinn, and Matthias Bethge · 2007
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Sparse log Gaussian processes via MCMC for spatial epidemiology
Jarno Vanhatalo and Aki Vehtari · 2007
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Twin Gaussian processes for structured prediction
Liefeng Bo and Cristian Sminchisescu · 2010
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Local Gaussian processes for pose recognition from noisy inputs
Martin Fergie and Aphrodite Galata · 2010
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Fundamental Statistics for the Behavioral Sciences
David C. Howell · 2010
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Gaussian processes for object categorization
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, and Trevor Darrell · 2010
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Gaussian processes for machine learning (GPML) toolbox
Carl Edward Rasmussen and Hannes Nickisch · 2010
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Spiked Dirchlet process priors for Gaussian process models
Terrance Savitsky and Marina Vannucci · 2010
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Approximate inference for disease mapping with sparse Gaussian processes
Jarno Vanhatalo, Ville Pietiläinen, and Aki Vehtari · 2010
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Multi-task warped Gaussian process for personalized age estimation
Yu Zhang and Dit-Yan Yeung · 2010
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Bayesian generalized kernel models
Zhihua Zhang, G. Dai, D. Wang, and M. I. Jordan · 2010
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Generalized Gaussian process models
Antoni B. Chan and Daxiang Dong · 2011
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Dirichlet process mixtures of generalized linear models
Lauren A. Hannah, David M. Blei, and Warren B. Powell · 2011
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Robust Gaussian process regression with a student-t likelihood
Pasi Jylänki, Jarno Vanhatal, and Aki Vehtari · 2011
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Variable selection for nonparametric Gaussian process priors: Models and computational strategies
Terrance Savitsky, Marina Vannucci, and Naijun Sha · 2011
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Gaussian Process Regression Analysis for Functional Data
Jian Qing Shi and Taeryon Choi · 2011
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Bayesian modeling with Gaussian processes using the MATLAB toolbox GP- stuff
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, and Aki Vehtari · 2011
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Supplemental material of “on approximate inference for generalized gaussian process models”
A. B. Chan · 2013
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