def METHOD_NAME(self, predicted_state, measurement_model=None, **kwargs):
    r"""There's no direct analogue of a predicted measurement in the information form. This
    method is therefore provided to return the predicted measurement as would the standard
    Kalman updater. This is mainly for compatibility as it's not anticipated that it would
    be used in the usual operation of the information filter.
    Parameters
    ----------
    predicted_information_state : :class:`~.State`
        The predicted state in information form :math:`\mathbf{y}_{k|k-1}`
    measurement_model : :class:`~.MeasurementModel`
        The measurement model. If omitted, the model in the updater object
        is used
    **kwargs : various
        These are passed to :meth:`~.MeasurementModel.matrix()`
    Returns
    -------
    : :class:`~.GaussianMeasurementPrediction`
        The measurement prediction, :math:`H \mathbf{x}_{k|k-1}`
    """
    # If a measurement model is not specified then use the one that's
    # native to the updater
    measurement_model = self._check_measurement_model(measurement_model)
    hh = self._measurement_matrix(predicted_state=predicted_state,
                                  measurement_model=measurement_model,
                                  **kwargs)
    predicted_covariance = np.linalg.inv(predicted_state.precision)
    predicted_state_mean = predicted_covariance @ predicted_state.state_vector
    predicted_measurement = hh @ predicted_state_mean
    innovation_covariance = hh @ predicted_covariance @ hh.T + measurement_model.covar()
    return GaussianMeasurementPrediction(predicted_measurement, innovation_covariance,
                                         predicted_state.timestamp,
                                         cross_covar=predicted_covariance @ hh.T)