Make an update of the internal status by gathering the variational posteriors for all the individual models.

def _update_inernal(self, varp_list):
        """Make an update of the internal status by gathering the variational posteriors for all the individual models."""
        # The probability for the binary variable for the same latent dimension of any of the models is on.
        if self.group_spike:
            self._b_prob_all = 1.-param_to_array(varp_list[0].gamma_group)
            [np.multiply(self._b_prob_all, 1.-vp.gamma_group, self._b_prob_all) for vp in varp_list[1:]]
        else:
            self._b_prob_all = 1.-param_to_array(varp_list[0].binary_prob)
            [np.multiply(self._b_prob_all, 1.-vp.binary_prob, self._b_prob_all) for vp in varp_list[1:]]