Woodbury vector in the gaussian likelihood case only is defined as
        $$
        (K_{xx} + \Sigma)^{-1}Y
        \Sigma := \texttt{Likelihood.variance / Approximate likelihood covariance}
        $$

def woodbury_vector(self):
        """
        Woodbury vector in the gaussian likelihood case only is defined as
        $$
        (K_{xx} + \Sigma)^{-1}Y
        \Sigma := \texttt{Likelihood.variance / Approximate likelihood covariance}
        $$
        """
        if self._woodbury_vector is None:
            self._woodbury_vector, _ = dpotrs(self.K_chol, self.mean - self._prior_mean)
        return self._woodbury_vector