atrix <tt>K</tt> and the mean vector <tt>m</tt> by calling the
corresponding functions with the hyperparameters and the input
locations <tt>x</tt>. Finally, the targets <tt>y</tt> are computed by
drawing randomly from a Gaussian with the desired covariance and mean
and adding Gaussian noise with standard deviation
<tt>exp(hyp.lik)</tt>. The above code is a bit special because we
explicitly call the mean and covariance functions (in order to
generate samples from a GP); ordinarily, we would only directly cal