code: def _logL_delays(self, delays_model, delays_measured, delays_errors):
          
          delta_t_model = np.array(delays_model[1:]) - delays_model[0]
          logL = np.sum(-(delta_t_model - delays_measured) ** 2 / (2 * delays_errors ** 2))
          return logL
docs: log likelihood of modeled delays vs measured time delays under considerations of errors
  
          :param delays_model: n delays of the model (not relative delays)
          :param delays_measured: relative delays (1-2,1-3,1-4) relative to the first in the list
          :param delays_errors: gaussian errors on the measured delays
          :return: log likelihood of data given model