Gaussian Process : introduction

   
Gaussian process Wikipedia  
Kriging Wikipedia  
Regression-kriging Wikipedia  
   
   
Gaussian Processes: A Quick Introduction Mark Ebden
   
Basics of Gaussian Processes  
   
Gaussian Processes in Practice Workshop Bletchley Park, U.K. 12 – 13 June 2006
   
Gaussian Processes for Dummies Katherine Bailey
   
The Gaussian Processes Web Site  
   
Gaussian Processes in Machine Learning Carl Edward Rasmussen – Max Planck Institute for Biological Cybernetics
Prediction With Gaussian Processes: From Linear Regression To Linear Prediction And Beyond (1997) C. K. I. Williams
Bayesian Classification With Gaussian Processes Christopher K.I. Williams  and David Barber
Sparse Online Gaussian Processes Lehel Csat´o and Manfred Opper
Regression and Classification Using Gaussian Process Priors RADFORD M. NEAL
   
   
   
Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams (book)
   
Gaussian Processes in Machine Learning Carl Edward Rasmussen
   
Bayesian inference and Gaussian processes Carl Edward Rasmussen, Max Planck Institute (video)
   
Learning with Gaussian Processes Carl Edward Rasmussen, Max Planck Institute (video)
   
Advances in Gaussian Processes Carl Edward Rasmussen (slides & videos)
   
Gaussian Process Basics David MacKay, University of Cambridge (video)
Introduction to Gaussian Processes  
Gaussian Processes – A Replacement for Supervised Neural Networks?  
Variational Gaussian Process Classifiers  
Efficient implementation of Gaussian processes  
   
   
Machine learning – Introduction to Gaussian processes  Nando de Freitas (University of Oxford)
GP Tutorial Conference on Computer Vision and Pattern Recognition CVPR 2012 – Providence, Rhode Island, USA – Saturday June 16, 2012
   
Gaussian Process for regression : a tutorial  José Melo – Faculty of Engineering, University of Porto
   
Gaussian Kernel Smoothing Moo K. Chung
   
Introduction to Gaussian Processes Barnabás Póczos University of Alberta
   
Gaussian Processes Neil D. Lawrence and Raquel Urtasun (University of Toronto)
   
A Tutorial on Gaussian Process Danushka Bollegala – The University of Tokyo
   
Gaussian Processes in Practice Proceedings of Machine Learning Research – Volume 1
   
Tutorial: Gaussian process models for machine learning Ed Snelson UCL (University College London)
Flexible and efficient Gaussian process models for machine learning Edward Lloyd Snelson (PhD Thesis)
   
Non-parametric Bayesian Methods Pr. Zoubin Ghahramani (University of Cambridge)
   
Understanding Gaussian Process Regression Using the Equivalent Kernel Peter Sollich (Dept of Mathematics, King’s College London)  and Christopher K. I. Williams (School of Informatics, University of Edinburgh)
   
Gaussian Processes Daniel McDuff (MIT Media Lab)
   
Introduction to Gaussian Processes Iain Murray University Toronto)
   
CVPR 2012 Tutorial: All you want to know about Gaussian Processes Conference on Computer Vision and Pattern Recognition CVPR 2012 – Providence, Rhode Island, USA – Saturday June 16, 2012
   
Workshop on Gaussian Processes for Feature Extraction University of Sheffield 18th September 2014
   
Gaussian Process Summer Schools University of Sheffield
   
Tutorial on Gaussian Processes and the Gaussian Process Latent  Andreas Damianou (Department of Neuro- and Computer Science, University of Sheffield, UK)
Gaussian processes for data-driven modelling and uncertainty quantication: a hands-on tutorial  
Deep Gaussian processes  
Feature representation with Deep Gaussian processes  
Probabilistic Models for Learning Data Representations  
Bayesian latent variable modelling with Gaussian processes Neil Lawrence, Andreas Damianou: GPs and Latent Variable Models video
System identi cation and control with (deep)
Gaussian processes		  
GPs and Latent Variable Models  video
   
   
Gaussian Processes for Machine Learning Matthias Seeger
Department of EECS
University of California at Berkeley
   
Tutorial on Gaussian Processes with applications to medical data 13 – 24 July 2015 at Medical Imaging and Computer Assisted Interventions 2015, Munich
   
Introduction to Gaussian Process Regression Hanna M. Wallach
   
Bayesian Learning with Gaussian Processes for Supervised Classification of Hyperspectral Data Kaiguang Zhao, Sorin Popescu, and Xuesong Zhang
   
Patchwork Kriging for Large-scale Gaussian Process Regression Chiwoo Park and Daniel Apley
   
Nested Kriging estimations for datasets with large number of observations Didier Rullière , Nicolas Durrande, François Bachoc and Clément Chevalier
   
Gaussian Process Regression
with Location Errors		 Daniel Cervone and Natesh S. Pillaiy
   
Polynomial-Chaos-based Kriging Roland Schobi, Bruno Sudret,  and Joe Wiart
   
A Novel Approach to Forecasting Financial
Volatility with Gaussian Process Envelopes		 Syed Ali Asad Rizvi, Stephen J. Roberts,
Michael A. Osborne, and Favour Nyikosa
   
Gaussian Process Regression Model for Distribution
Inputs		 Francois Bachoc, Fabrice Gamboa, Jean-Michel Loubes and Nil Venet
   
Cross Validation and Maximum Likelihood estimations
of hyper-parameters of Gaussian processes with model
misspecification		 Francois Bachoc (Associate professor at the Toulouse Mathematics Institute and the University Paul Sabatier )
   
Reliability-based design optimization
using kriging surrogates and subset simulation		 V. Dubourg · B. Sudret · J.-M. Bourinet
   
Generative Kriging Surrogate Model for Constrained
and Unconstrained Multi-objective Optimization		 Rayan Hussein and Kalyanmoy Deb
   
   
A Multivariate Interpolation and Regression Enhanced
Kriging Surrogate Model		 Komahan Boopathy  and Markus P. Rumpfkeil
   
Crack identification based on Kriging surrogate mode Hai-yang Gao, Xing-lin Guoa and Xiao-fei Hu
   
Application Of Kriging Method In Surrogate Management
Framework For Optimization Problems		 B. Azarkhalili, M. Rasouli, P. Moghadas, and B. Mehri
   
Application of Latin Hypercube Sampling Based Kriging
Surrogate Models in Reliability Assessment		 Liu Chu, Eduardo Souza De Cursi, Abdelkhalak El Hami, Mohamed Eid