Nested sampling for frequentist computation: fast estimation of small $p$-values

Abstract

We propose a novel method for computing $p$-values based on nested sampling (NS) applied to the sampling space rather than the parameter space of the problem, in contrast to its usage in Bayesian computation. The computational cost of NS scales as ${\log }^{2}1/p$, which compares favorably to the $1/p$ scaling for Monte Carlo (MC) simulations. For significances greater than about $4\sigma$ in both a toy problem and a simplified resonance search, we show that NS requires orders of magnitude fewer simulations than ordinary MC estimates. This is particularly relevant for high-energy physics, which adopts a $5\sigma$ gold standard for discovery. We conclude with remarks on new connections between Bayesian and frequentist computation and possibilities for tuning NS implementations for still better performance in this setting.

5 pages + refs, 3 figures. added refs + schematic of algorithm. closely matches published version

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Cited by in corpus (4)

• Nested sampling for physical scientists
• A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications
• Exploring phase space with Nested Sampling
• An importance sampling method for Feldman-Cousins confidence intervals