The inverse of the woodbury matrix, in the gaussian likelihood case it is defined as
        $$
        (K_{xx} + \Sigma_{xx})^{-1}
        \Sigma_{xx} := \texttt{Likelihood.variance / Approximate likelihood covariance}
        $$

def woodbury_inv(self):
        """
        The inverse of the woodbury matrix, in the gaussian likelihood case it is defined as
        $$
        (K_{xx} + \Sigma_{xx})^{-1}
        \Sigma_{xx} := \texttt{Likelihood.variance / Approximate likelihood covariance}
        $$
        """
        if self._woodbury_inv is None:
            if self._woodbury_chol is not None:
                self._woodbury_inv, _ = dpotri(self._woodbury_chol, lower=1)
                # self._woodbury_inv, _ = dpotrs(self.woodbury_chol, np.eye(self.woodbury_chol.shape[0]), lower=1)
                symmetrify(self._woodbury_inv)
            elif self._covariance is not None:
                B = np.atleast_3d(self._K) - np.atleast_3d(self._covariance)
                self._woodbury_inv = np.empty_like(B)
                for i in range(B.shape[-1]):
                    tmp, _ = dpotrs(self.K_chol, B[:, :, i])
                    self._woodbury_inv[:, :, i], _ = dpotrs(self.K_chol, tmp.T)
        return self._woodbury_inv