t a priori know the values of the
hyperparameters <tt>hyp</tt>, let alone the form of the mean,
covariance or likelihood functions. So, let's pretend we didn't know
any of this. We assume a particular structure and learn suitable
hyperparameters:</p>

<pre>
  covfunc = @covSEiso; hyp2.cov = [0; 0]; hyp2.lik = log(0.1);

  hyp2 = minimize(hyp2, @gp, -100, @infExact, [], covfunc, likfunc, x, y);
  exp(hyp2.lik)
  nlml2 = gp(hyp2, @infExact, [], covfunc, likfunc, x, y)

  [m s2] = gp(hyp2, @infExact, [], covfu