docstr: Return a instance of the predictive distribution.
  
  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
  -------
  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.
docstr_tok: ["Return","a","instance","of","the","predictive","distribution",".","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","--","--","--","-","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","."]
code: def get_distribution(self, params, exog=None, exog_precision=None):
      """
      Return a instance of the predictive distribution.
  
      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
      -------
      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.
      """
      from scipy import stats
  
      args = self.get_distribution_params(
          params, exog=exog, exog_precision=exog_precision
      )
      distr = stats.beta(*args)
      return distr
  
imports: import numpy
  from scipy.special import gammaln
  import patsy
  import statsmodels.base.wrapper
  import statsmodels.regression.linear_model
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.base.model import GenericLikelihoodModel, GenericLikelihoodModelResults, _LLRMixin
  from statsmodels.genmod import families
label_desc: Use image node_id 21 for calling the BetaModel obj's underlying member method code with example usage: obj.get_distribution(params, exog, exog_precision) and returns: distr