code: def __contribution_from_parameters(self, parameter_names):
          
  
  
          #get the prior and posterior for the base case
          bprior,bpost = self.prior_prediction, self.posterior_prediction
          #get the prior and posterior for the conditioned case
          la_cond = self.get_conditional_instance(parameter_names)
          cprior,cpost = la_cond.prior_prediction, la_cond.posterior_prediction
          return cprior,cpost
docs: private method get the prior and posterior uncertainty reduction as a result of
          some parameter becoming perfectly known
  
          Parameters
          ----------
          parameter_names : list
              parameter that are perfectly known
  
          Returns
          -------
          dict : dict
              dictionary of forecast name,  [prior uncertainty w/o parameter_names,
                  % posterior uncertainty w/o parameter names]
  
          Note
          ----
          this method is used by get_parameter_contribution() method - don't
          call this method directly