Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual demgpard Daniel@0: Daniel@0: Daniel@0: Daniel@0:

demgpard Daniel@0:

Daniel@0:

Daniel@0: Purpose Daniel@0:

Daniel@0: Demonstrate ARD using a Gaussian Process. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

Daniel@0: 
Daniel@0: demgpare
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Daniel@0: Description Daniel@0:

Daniel@0: The data consists of three input variables x1, x2 and Daniel@0: x3, and one target variable Daniel@0: t. The Daniel@0: target data is generated by computing sin(2*pi*x1) and adding Gaussian Daniel@0: noise, x2 is a copy of x1 with a higher level of added Daniel@0: noise, and x3 is sampled randomly from a Gaussian distribution. Daniel@0: A Gaussian Process, is Daniel@0: trained by optimising the hyperparameters Daniel@0: using the scaled conjugate gradient algorithm. The final values of the Daniel@0: hyperparameters show that the model successfully identifies the importance Daniel@0: of each input. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: demgp, 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: