Gaussian Process

This page contains resources about Gaussian Processes (GP), including GP Classification and GP Regression (kriging).

Lecture Notes

 * Probabilistic Machine Learning by Carl Edward Rasmussen

Books and Book Chapters

 * MacKay, D. J. (2003). "Chapter 45: Gaussian Processes". Information Theory, Inference and Learning Algorithms. Cambridge University Press.
 * Williams, C. K., & Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning. MIT Press.
 * Murphy, K. P. (2012). "Chapter 15: Gaussian Processes". Machine Learning: A Probabilistic Perspective. MIT Press.
 * Barber, D. (2012). "Chapter 19: Gaussian Processes". Bayesian Reasoning and Machine Learning. Cambridge University Press.

Scholarly Articles

 * Rasmussen, C. E. (2004). Gaussian Processes in Machine Learning. In Advanced Lectures on Machine Learning. Springer Berlin Heidelberg.
 * Seeger, M. (2004). Gaussian processes for machine learning. International journal of neural systems, 14(02), 69-106.
 * Rasmussen, C. E., & Nickisch, H. (2010). Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research, 11(Nov), 3011-3015.

Software

 * GPflow - Python with TensorFlow
 * GPML - MATLAB code for the book by Williams & Rasmussen
 * GPy - Python
 * GPmat - MATLAB
 * Gaussian Processes (scikit-learn) - Python
 * Gaussian Process Regression (Statistics and Machine Learning Toolbox) - MATLAB
 * pyKriging - Python
 * PyGP - A Gaussian Process Toolbox in Python

Other Resources

 * Gaussian Processes for Machine Learning - Book webpage
 * Sheffield Machine Learning Software - Python and MATLAB
 * Gaussian Processes - Zoubin Ghahramani