def get_distribution(self, exog=None, exog_precision=None, transform=True):
    '\n        Return a instance of the predictive distribution.\n\n        Parameters\n        ----------\n        exog : array_like\n            Array of predictor variables for mean.\n        exog_precision : 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        Instance of a scipy frozen distribution based on estimated\n        parameters.\n\n        See Also\n        --------\n        predict\n\n        Notes\n        -----\n        This function delegates to the predict method to handle exog and\n        exog_precision, which in turn makes any required transformations.\n\n        Due to the behavior of ``scipy.stats.distributions objects``, the\n        returned random number generator must be called with ``gen.rvs(n)``\n        where ``n`` is the number of observations in the data set used\n        to fit the model.  If any other value is used for ``n``, misleading\n        results will be produced.\n        '
    from scipy import stats
    args = self.get_distribution_params(exog=exog, exog_precision=exog_precision, transform=transform)
    args = (np.asarray(arg) for arg in args)
    distr = stats.beta(*args)
    return distr
Return a instance of the predictive distribution.

Parameters
----------
exog : array_like
    Array of predictor variables for mean.
exog_precision : 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
-------
Instance of a scipy frozen distribution based on estimated
parameters.

See Also
--------
predict

Notes
-----
This function delegates to the predict method to handle exog and
exog_precision, which in turn makes any required transformations.

Due to the behavior of ``scipy.stats.distributions objects``, the
returned random number generator must be called with ``gen.rvs(n)``
where ``n`` is the number of observations in the data set used
to fit the model.  If any other value is used for ``n``, misleading
results will be produced.
def get_distribution(self, exog=None, exog_precision=None, transform=True):
    '\n        Return a instance of the predictive distribution.\n\n        Parameters\n        ----------\n        exog : array_like\n            Array of predictor variables for mean.\n        exog_precision : 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        Instance of a scipy frozen distribution based on estimated\n        parameters.\n\n        See Also\n        --------\n        predict\n\n        Notes\n        -----\n        This function delegates to the predict method to handle exog and\n        exog_precision, which in turn makes any required transformations.\n\n        Due to the behavior of ``scipy.stats.distributions objects``, the\n        returned random number generator must be called with ``gen.rvs(n)``\n        where ``n`` is the number of observations in the data set used\n        to fit the model.  If any other value is used for ``n``, misleading\n        results will be produced.\n        '
    from scipy import stats
    args = self.get_distribution_params(exog=exog, exog_precision=exog_precision, transform=transform)
    args = (np.asarray(arg) for arg in args)
    distr = stats.beta(*args)
    return distr