def METHOD_NAME(self):
    M_inv = np.array([[1, -0.5, 1], [-0.5, 3, 0], [1, 0, 2]])
    d_prior = 1000
    m = len(M_inv)
    log_det = de_lens.marginalization_new(M_inv, d_prior=d_prior)
    log_det_old = de_lens.marginalisation_const(M_inv)
    npt.assert_almost_equal(
        log_det,
        log_det_old + m / 2.0 * np.log(np.pi / 2.0) - m * np.log(d_prior),
        decimal=9,
    )
    M_inv = np.array([[1, 1, 1], [0.0, 1.0, 0.0], [1.0, 2.0, 1.0]])
    log_det = de_lens.marginalization_new(M_inv, d_prior=10)
    log_det_old = de_lens.marginalisation_const(M_inv)
    npt.assert_almost_equal(log_det, log_det_old, decimal=9)
    npt.assert_almost_equal(log_det, -(10 ** (15)), decimal=10)
    log_det = de_lens.marginalization_new(M_inv, d_prior=None)
    log_det_old = de_lens.marginalisation_const(M_inv)
    npt.assert_almost_equal(log_det, log_det_old, decimal=9)