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