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arXiv · 2303.06317

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

Ryan Giordano·Runjing Liu·Michael I. Jordan·Tamara Broderick
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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.

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Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick. 2023-03-11. Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (Rejoinder). https://arxiv.org/abs/2303.06317

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