def METHOD_NAME(UpdaterClass, measurement_model, prediction, measurement, layer_width):
    # Calculate evaluation variables
    eval_measurement_prediction = GaussianMeasurementPrediction(
        measurement_model.matrix() @ prediction.mean,
        measurement_model.matrix() @ prediction.covar
        @ measurement_model.matrix().T
        + measurement_model.covar(),
        cross_covar=prediction.covar @ measurement_model.matrix().T)
    innovation_vector = measurement.state_vector - eval_measurement_prediction.state_vector
    kalman_gain = np.linalg.pinv(measurement_model.matrix()) \
        @ np.diag(np.clip(np.abs(innovation_vector) / layer_width, -1, 1).ravel())
    I_KH = np.identity(prediction.ndim) - kalman_gain @ measurement_model.matrix()
    posterior_covariance = \
        I_KH @ prediction.covar @ I_KH.T + kalman_gain @ measurement_model.covar() @ kalman_gain.T
    eval_posterior = GaussianState(
        prediction.mean + kalman_gain@(measurement.state_vector-eval_measurement_prediction.mean),
        posterior_covariance)
    # Initialise a updater
    updater = UpdaterClass(measurement_model=measurement_model, layer_width=layer_width)
    # Get and assert measurement prediction
    measurement_prediction = updater.predict_measurement(prediction)
    assert(np.allclose(measurement_prediction.mean,
                       eval_measurement_prediction.mean,
                       0, atol=1.e-14))
    assert(np.allclose(measurement_prediction.covar,
                       eval_measurement_prediction.covar,
                       0, atol=1.e-14))
    assert(np.allclose(measurement_prediction.cross_covar,
                       eval_measurement_prediction.cross_covar,
                       0, atol=1.e-14))
    # Perform and assert state update (without measurement prediction)
    posterior = updater.update(SingleHypothesis(
        prediction=prediction,
        measurement=measurement))
    assert(np.allclose(posterior.mean, eval_posterior.mean, 0, atol=1.e-14))
    assert(np.allclose(posterior.covar, eval_posterior.covar, 0, atol=1.e-14))
    assert(np.array_equal(posterior.hypothesis.prediction, prediction))
    assert (np.allclose(
        posterior.hypothesis.measurement_prediction.state_vector,
        measurement_prediction.state_vector, 0, atol=1.e-14))
    assert (np.allclose(posterior.hypothesis.measurement_prediction.covar,
                        measurement_prediction.covar, 0, atol=1.e-14))
    assert(np.array_equal(posterior.hypothesis.measurement, measurement))
    assert(posterior.timestamp == prediction.timestamp)
    # Perform and assert state update
    posterior = updater.update(SingleHypothesis(
        prediction=prediction,
        measurement=measurement,
        measurement_prediction=measurement_prediction))
    assert(np.allclose(posterior.mean, eval_posterior.mean, 0, atol=1.e-14))
    assert(np.allclose(posterior.covar, eval_posterior.covar, 0, atol=1.e-14))
    assert(np.array_equal(posterior.hypothesis.prediction, prediction))
    assert (np.allclose(
        posterior.hypothesis.measurement_prediction.state_vector,
        measurement_prediction.state_vector, 0, atol=1.e-14))
    assert (np.allclose(posterior.hypothesis.measurement_prediction.covar,
                        measurement_prediction.covar, 0, atol=1.e-14))
    assert(np.array_equal(posterior.hypothesis.measurement, measurement))
    assert(posterior.timestamp == prediction.timestamp)