code: def prior_prediction(self):
          
          if self.__prior_prediction is not None:
              return self.__prior_prediction
          else:
              if self.predictions is not None:
                  self.log("propagating prior to predictions")
                  prior_cov = self.predictions.T *\
                                  self.parcov * self.predictions
                  self.__prior_prediction = {n:v for n,v in
                                            zip(prior_cov.row_names,
                                                np.diag(prior_cov.x))}
                  self.log("propagating prior to predictions")
              else:
                  self.__prior_prediction = {}
              return self.__prior_prediction
docs: get a dict of prior prediction variances
  
          Returns
          -------
          prior_prediction : dict
              dictionary of prediction name, prior variance pairs