- GP regression uses GPs for function regression
- a GP is a stochastic process, that is fully specified by a mean and a covariance function
- the covariance function is specified by some kernel funktion k(t,t')
- it specifies how the function values f(t) and f(t') can change, for given arguments t and t'
- with this, a GP defines us a family of functions, not just one
- we can draw randomly samples from such a GP to see examples of functions
- now for GP regression you have some example measurements of your function
- given by {t1,…,tn} and {y1=f(t1),…,yn=f(tn)}
- you can now compute the mean- and covariance-function of a new GP that is restricted to that measurements
- that is what GP regression is all about
- the new GP will have small variance of function values nearby to the measurements and larger far away from that measurements

public/gaussian_process_regression.txt · Last modified: 2014/01/18 10:24 (external edit) · []