estimate the Akaike Information Criterion

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            output data vector
        mu : array-like of shape (n_samples,),
            expected value of the targets given the model and inputs
        weights : array-like shape (n_samples,) or None, optional
            containing sample weights
            if None, defaults to array of ones

        Returns
        -------
        None

def _estimate_AIC(self, y, mu, weights=None):
        """
        estimate the Akaike Information Criterion

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            output data vector
        mu : array-like of shape (n_samples,),
            expected value of the targets given the model and inputs
        weights : array-like shape (n_samples,) or None, optional
            containing sample weights
            if None, defaults to array of ones

        Returns
        -------
        None
        """
        estimated_scale = not(self.distribution._known_scale) # if we estimate the scale, that adds 2 dof
        return -2*self._loglikelihood(y=y, mu=mu, weights=weights) + \
                2*self.statistics_['edof'] + 2*estimated_scale