def METHOD_NAME(self, samples):
    """
    Calculates a measure of normed distance of samples from exact mean and
    covariance matrix assuming uniform prior with bounds given by
    :meth:`suggested_bounds`.
    See :meth:`ToyLogPDF.distance()`.
    """
    # 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()))
    # calculate normed distance
    d = list(map(lambda x: np.linalg.norm(x), samples))
    dist = (
        np.abs(self.mean_normed() - np.mean(d)) +
        np.abs(self.var_normed() - np.var(d))
    )
    return dist