def posteriori_mean(self):
from numpy_sugar.linalg import rsolve<EOL>Sigma = self.posteriori_covariance()<EOL>eta = self._ep._posterior.eta<EOL>return dot(Sigma, eta + rsolve(GLMM.covariance(self), self.mean()))<EOL>
r""" Mean of the estimated posteriori.

        This is also the maximum a posteriori estimation of the latent variable.