Gaussian process regression

(1.3 hours to learn)


Gaussian process regression is a Bayesian model for nonparametric regression. (That is, nonparametric in the sense that the complexity of the regression function grows with the amount of data.) The model places a prior directly on the output values without reference to an underlying parametric model.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Gaussian Processes for Machine Learning
A graduate-level machine learning textbook focusing on Gaussian processes.
Authors: Carl E. Rasmussen,Christopher K. I. Williams


Supplemental resources (the following are optional, but you may find them useful)


Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Author: David Barber


See also