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.

翻译：通常，我们可以直接针对特定问题构建局部稳健性指标，无论是用于马尔可夫链蒙特卡洛应用，还是变分贝叶斯等优化问题。然而，我们认为，仅仅构建局部稳健性指标是不够的：关键在于证明其有用性。可计算性、可解释性以及局部稳健性指标的良好外推能力，比单纯计算导数更为重要——且通常更难实现。