Confidence sequences for sampling without replacement Ian Waudby-Smith
–Neural Information Processing Systems
We present a generic approach to constructing a frequentist CS using Bayesian tools, based on the fact that the ratio of a prior to the posterior at the ground truth is a martingale. We then present Hoeffding-and empirical-Bernstein-type time-uniform CSs and fixed-time confidence intervals for sampling WoR, which improve on previous bounds in the literature and explicitly quantify the benefit of WoR sampling.
Neural Information Processing Systems
Feb-7-2025, 17:06:07 GMT
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- Research Report (0.69)