docs: def fit_mle(self, data, k_agg0=0.5):
          """%(super)s
  
          In addition to data, gives an optional keyword argument k_agg0 that
          specifies the initial value of k_agg used in the optimization.
  
          """
  
          mu = np.mean(data)
  
          def mle(k):
  
              return -np.sum(np.log(self.pmf(data, mu, k)))
  
          k = optim.fmin(mle, x0=k_agg0, disp=0)
  
          return mu, k[0]