a matrix <CODE>y</CODE> of output
vectors.  Each row of <CODE>x</CODE> corresponds to one input vector and each
row of <CODE>y</CODE> corresponds to one output vector.  This assumes that the
training data (both inputs and targets) has been stored in <CODE>net</CODE> by
a call to <CODE>gpinit</CODE>; these are needed to compute the training
data covariance matrix.

<p><CODE>[y, sigsq] = gpfwd(net, x)</CODE> also generates a column vector <CODE>sigsq</CODE> of
conditional variances (or squared error bars) whe