Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (Rejoinder)

Abstract

One can typically form a local robustness metric for a particular problem quite directly, for Markov chain Monte Carlo applications as well as optimization problems such as variational Bayes. However, we argue that simply forming a local robustness metric is not enough: the hard work is showing that it is useful. Computability, interpretability, and the ability of a local robustness metric to extrapolate well, are more important -- and often more difficult to establish -- than mere computation of derivatives.