def get_distribution_params(self, params, exog=None, exog_precision=None):
    '\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        exog_precision : array_like\n            Array of predictor variables for mean.\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(params, exog=exog)
    precision = self.predict(params, exog_precision=exog_precision, which='precision')
    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.
exog_precision : array_like
    Array of predictor variables for mean.

Returns
-------
(alpha, beta) : tuple of ndarrays
    Parameters for the scipy distribution to evaluate predictive
    distribution.
def get_distribution_params(self, params, exog=None, exog_precision=None):
    '\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        exog_precision : array_like\n            Array of predictor variables for mean.\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(params, exog=exog)
    precision = self.predict(params, exog_precision=exog_precision, which='precision')
    return ((precision * mean), (precision * (1 - mean)))