%(super)s

        Additional Parameters
        ----------------------
        b : float
            The upper limit of the distribution

def fit_mle(self, data, b=None):
        """%(super)s

        Additional Parameters
        ----------------------
        b : float
            The upper limit of the distribution
        """
        # Take mean of data as MLE of distribution mean, then calculate p
        mu = np.mean(data)
        if not b:
            b = np.sum(data)
        lam = _expon_solve_lam_from_mu_vect(mu, b)

        # Just return float, not len 1 array
        if len(np.atleast_1d(lam)) == 1:
            return float(lam), b
        else:
            return lam, b