2023/03/11 by Ryan Giordano, Runjing Liu, Giordano, Ryan +5
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2303.06317
openalex publication_date 2023/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.