'The variational lower bound for the mean parameters'

def _bound_means(self):

    logprior = 0.0
    logprior -= (0.5 * squared_norm(self.means_))
    logprior -= ((0.5 * self.means_.shape[1]) * self.n_components)
    return logprior
