Discussion of 'Bayesian Nonparametric Inference - Why and How', by Peter Müller and Riten Mitra.

Muller and Mitra’s contribution regarding the practical whys and hows of non-parametric Bayes (NPB) is welcome. In that spirit, we highlight one basic and one complex social science example for which NPB is uniquely well-suited. Depression symptoms scores were collected from n = 299 clients on three occasions – pre-treatment, post-treatment, and follow-up – during a study of group therapy’s effectiveness for treating depression. Clients completed up to four group therapy modules and could join the therapy group at start of a module. Therapy group-induced correlations among client outcomes could thus be modeled using random module effects, which would be linked to post-treatment outcomes via multiple membership, and client-specific growth parameters (e.g., random intercept, time, and quadratic time effects) could be specified for modeling within-client correlations and deviations from the average depression score trajectory (Paddock and Savitsky 2013).