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+function [e, edata, eprior] = gperr(net, x, t)
+%GPERR	Evaluate error function for Gaussian Process.
+%
+%	Description
+%	E = GPERR(NET, X, T) takes a Gaussian Process data structure NET
+%	together  with a matrix X of input vectors and a matrix T of target
+%	vectors, and evaluates the error function E. Each row of X
+%	corresponds to one input vector and each row of T corresponds to one
+%	target vector.
+%
+%	[E, EDATA, EPRIOR] = GPERR(NET, X, T) additionally returns the data
+%	and hyperprior components of the error, assuming a Gaussian prior on
+%	the weights with mean and variance parameters PRMEAN and PRVARIANCE
+%	taken from the network data structure NET.
+%
+%	See also
+%	GP, GPCOVAR, GPFWD, GPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'gp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+cn = gpcovar(net, x);
+
+edata = 0.5*(sum(log(eig(cn, 'nobalance'))) + t'*inv(cn)*t);
+
+% Evaluate the hyperprior contribution to the error.
+% The hyperprior is Gaussian with mean pr_mean and variance
+% pr_variance
+if isfield(net, 'pr_mean')
+  w = gppak(net);
+  m = repmat(net.pr_mean, size(w));
+  if size(net.pr_mean) == [1 1]
+    eprior = 0.5*((w-m)*(w-m)');
+    e2 = eprior/net.pr_var;
+  else
+    wpr = repmat(w, size(net.pr_mean, 1), 1)';
+    eprior = 0.5*(((wpr - m').^2).*net.index);
+    e2 = (sum(eprior, 1))*(1./net.pr_var);
+  end
+else
+  e2 = 0;
+  eprior = 0;
+end
+
+e = edata + e2;
+