Gaussian Process is powerful non-parametric machine learning technique for constructing comprehensive probabilistic models of real world problems. They can be applied to geostatistics, supervised, unsupervised, reinforcement learning, principal component analysis, system identification and control, rendering music performance, optimization and many other tasks.
Gaussian processes would be a very nice addition.
Gaussian process: https://en.wikipedia.org/wiki/Gaussian_process Gaussian process prediction: https://en.wikipedia.org/wiki/Gaussian_process#Gaussian_process_prediction Gaussian process (External links): https://en.wikipedia.org/wiki/Gaussian_process#External_links
Gaussian Processes for Regression - A Quick Introduction: http://www.robots.ox.ac.uk/~mebden/reports/GPtutorial.pdf A Tutorial on Gaussian Processes (or why I don’t use SVMs): http://mlss2011.comp.nus.edu.sg/uploads/Site/lect1gp.pdf