%(super)s

        Additional Parameters
        ----------------------
        fix_mean : bool
            Default False.  If True, fixes mean before optimizing sigma

def fit_mle(self, data, fix_mean=False):
        """%(super)s

        Additional Parameters
        ----------------------
        fix_mean : bool
            Default False.  If True, fixes mean before optimizing sigma

        """

        if not fix_mean:
            sigma, _, scale = stats.lognorm.fit(data, floc=0)
            return np.log(scale), sigma

        else:
            mean = np.mean(data)

            # MLE fxn to be optmimized
            mle = lambda sigma, x, mean: -1 *\
                                    np.sum(self._pdf_w_mean(x, mean, sigma))

            sigma = optim.fmin(mle, np.array([np.std(np.log(data), ddof=1)]),
                                            args=(data, mean), disp=0)[0]

            return self.translate_args(mean, sigma)