def _raw_predict(self, Xnew, full_cov=False, kern=None):
mu, var = self.posterior._raw_predict(kern=self.kern if kern is None else kern, Xnew=Xnew, pred_var=self._predictive_variable, full_cov=full_cov)<EOL>if self.mean_function is not None:<EOL><INDENT>mu += self.mean_function.f(Xnew)<EOL><DEDENT>return mu, var<EOL>
For making predictions, does not account for normalization or likelihood

full_cov is a boolean which defines whether the full covariance matrix
of the prediction is computed. If full_cov is False (default), only the
diagonal of the covariance is returned.

.. math::
    p(f*|X*, X, Y) = \int^{\inf}_{\inf} p(f*|f,X*)p(f|X,Y) df
                = N(f*| K_{x*x}(K_{xx} + \Sigma)^{-1}Y, K_{x*x*} - K_{xx*}(K_{xx} + \Sigma)^{-1}K_{xx*}
    \Sigma := \texttt{Likelihood.variance / Approximate likelihood covariance}