code: def gaussian_priors_at_sigma_limit(self, sigma_limit):
          
  
          means = self.most_probable_from_summary()
          uppers = self.model_at_upper_sigma_limit(sigma_limit)
          lowers = self.model_at_lower_sigma_limit(sigma_limit)
  
          # noinspection PyArgumentList
          sigmas = list(map(lambda mean, upper, lower: max([upper - mean, mean - lower]), means, uppers, lowers))
  
          return list(map(lambda mean, sigma: (mean, sigma), means, sigmas))
docs: Compute the Gaussian Priors these results should be initialzed with in the next phase, by taking their \
          most probable values (e.g the means of their PDF) and computing the error at an input sigma_limit.
  
          Parameters
          -----------
          sigma_limit : float
              The sigma limit within which the PDF is used to estimate errors (e.g. sigma_limit = 1.0 uses 0.6826 of the \
              PDF).