def METHOD_NAME(self, samples):
    r"""
    Calculates the KL divergence of samples of the :math:`nu` parameter
    of Neal's funnel from the analytic :math:`\mathcal{N}(0, 3)` result.
    """
    # Check size of input
    if not len(samples.shape) == 2:
        raise ValueError('Given samples list must be n x 2.')
    if samples.shape[1] != self._n_parameters:
        raise ValueError(
            'Given samples must have length ' + str(self._n_parameters))
    nu = samples[:, self._n_parameters - 1]
    m0 = np.mean(nu)
    s0 = np.var(nu)
    return 0.5 * (np.sum(self._s1_inv * s0) +
                  (self._m1 - m0) * self._s1_inv * (self._m1 - m0) -
                  np.log(s0) +
                  np.log(self._s1) -
                  1)