Year: 2025
Type: article
Abstract: When combining apparently inconsistentexperimental results, one often implements errors on errors. The Particle Data Group’s phenomenological prescription offers a practical solution but lacks a firm theoretical foundation. To address this, D’Agostini and Cowan have proposed Bayesian and frequentist approaches, respectively, both introducing gamma-distributed auxiliary variables to model uncertainty in quoted errors. In this Letter, we show that these two formulations admit a parameter-by-parameter correspondence, and are structurally equivalent. This identification clarifies how Bayesian prior choices can be interpreted in terms of frequentist sampling assumptions, providing a unified probabilistic framework for modeling uncertainty in quoted variances.
Source: The European Physical Journal C
Authors: Satoshi Mishima, Kin-ya Oda
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414326906