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