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,<EOL>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
                = MVN\left(\nu + N,f*| K_{x*x}(K_{xx})^{-1}Y,
                \frac{\nu + \beta - 2}{\nu + N - 2}K_{x*x*} - K_{xx*}(K_{xx})^{-1}K_{xx*}\right)
    \nu := \texttt{Degrees of freedom}