Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demgp Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demgp Daniel@0:

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Daniel@0: Purpose Daniel@0:

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

Daniel@0: Synopsis Daniel@0:

Daniel@0: 
Daniel@0: demgp
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Daniel@0: Description Daniel@0:

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

Daniel@0: See Also Daniel@0:

Daniel@0: gp, gperr, gpfwd, gpgrad, gpinit, scg
Daniel@0: Pages: Daniel@0: Index Daniel@0:
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Copyright (c) Ian T Nabney (1996-9) Daniel@0: Daniel@0: Daniel@0: Daniel@0: