@inherit_docstring_from(rv_discrete_meco)<EOL><INDENT>def fit_mle(self, data, k_agg0=<NUM_LIT:0.5>):<DEDENT>
mu = np.mean(data)<EOL>def mle(k):<EOL><INDENT>return -np.sum(np.log(self.pmf(data, mu, k)))<EOL><DEDENT>k = optim.fmin(mle, x0=k_agg0, disp=<NUM_LIT:0>)<EOL>return mu, k[<NUM_LIT:0>]<EOL>
%(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.