  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.
