  In this discussion, we compare the choice of seeded intervals and that of
random intervals for change point segmentation from practical, statistical and
computational perspectives. Furthermore, we investigate a novel estimator of
the noise level, which improves many existing model selection procedures
(including the steepest drop to low levels), particularly for challenging
frequent change point scenarios with low signal-to-noise ratios.
