Bayesian nonparametrics (BNPs) are poised to have a deep impact in the analysis of single
molecule data as they provide posterior probabilities over entire models consistent with the
supplied data, not just model parameters of one preferred model. Thus they provide an
elegant and rigorous solution to the difficult problem encountered when selecting an
appropriate candidate model. Nevertheless, BNPs' flexibility to learn models and their
associated parameters from experimental data is a double-edged sword. Most importantly …