wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual demgp wolffd@0: wolffd@0: wolffd@0: wolffd@0:

demgp wolffd@0:

wolffd@0:

wolffd@0: Purpose wolffd@0:

wolffd@0: Demonstrate simple regression using a Gaussian Process. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

wolffd@0: 
wolffd@0: demgp
wolffd@0: wolffd@0: wolffd@0:

wolffd@0: Description wolffd@0:

wolffd@0: The problem consists of one input variable x and one target variable wolffd@0: t. The values in x are chosen in two separated clusters and the wolffd@0: target data is generated by computing sin(2*pi*x) and adding Gaussian wolffd@0: noise. Two Gaussian Processes, each with different covariance functions wolffd@0: are trained by optimising the hyperparameters wolffd@0: using the scaled conjugate gradient algorithm. The final predictions are wolffd@0: plotted together with 2 standard deviation error bars. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: gp, gperr, gpfwd, gpgrad, gpinit, scg
wolffd@0: Pages: wolffd@0: Index wolffd@0:
wolffd@0:

Copyright (c) Ian T Nabney (1996-9) wolffd@0: wolffd@0: wolffd@0: wolffd@0: