# demgp

## Purpose

Demonstrate simple regression using a Gaussian Process.

## Synopsis

    demgp

## Description

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

## See Also

`gp`, `gperr`, `gpfwd`, `gpgrad`, `gpinit`, `scg`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)