def get_distribution_params(self, exog=None, exog_precision=None, transform=True):
    '\n        Return distribution parameters converted from model prediction.\n\n        Parameters\n        ----------\n        params : array_like\n            The model parameters.\n        exog : array_like\n            Array of predictor variables for mean.\n        transform : bool\n            If transform is True and formulas have been used, then predictor\n            ``exog`` is passed through the formula processing. Default is True.\n\n        Returns\n        -------\n        (alpha, beta) : tuple of ndarrays\n            Parameters for the scipy distribution to evaluate predictive\n            distribution.\n        '
    mean = self.predict(exog=exog, transform=transform)
    precision = self.predict(exog_precision=exog_precision, which='precision', transform=transform)
    return ((precision * mean), (precision * (1 - mean)))
Return distribution parameters converted from model prediction.

Parameters
----------
params : array_like
    The model parameters.
exog : array_like
    Array of predictor variables for mean.
transform : bool
    If transform is True and formulas have been used, then predictor
    ``exog`` is passed through the formula processing. Default is True.

Returns
-------
(alpha, beta) : tuple of ndarrays
    Parameters for the scipy distribution to evaluate predictive
    distribution.
def get_distribution_params(self, exog=None, exog_precision=None, transform=True):
    '\n        Return distribution parameters converted from model prediction.\n\n        Parameters\n        ----------\n        params : array_like\n            The model parameters.\n        exog : array_like\n            Array of predictor variables for mean.\n        transform : bool\n            If transform is True and formulas have been used, then predictor\n            ``exog`` is passed through the formula processing. Default is True.\n\n        Returns\n        -------\n        (alpha, beta) : tuple of ndarrays\n            Parameters for the scipy distribution to evaluate predictive\n            distribution.\n        '
    mean = self.predict(exog=exog, transform=transform)
    precision = self.predict(exog_precision=exog_precision, which='precision', transform=transform)
    return ((precision * mean), (precision * (1 - mean)))