Implementing Errors on Errors: Bayesian vs Frequentist

Fuente: arXiv
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Main Authors: Mishima, Satoshi, Oda, Kin-ya
Format: Preprint
Published: 2025
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author Mishima, Satoshi
Oda, Kin-ya
author_facet Mishima, Satoshi
Oda, Kin-ya
contents When combining apparently inconsistent experimental 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_06521
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Implementing Errors on Errors: Bayesian vs Frequentist
Mishima, Satoshi
Oda, Kin-ya
High Energy Physics - Phenomenology
Instrumentation and Methods for Astrophysics
High Energy Physics - Experiment
Methodology
When combining apparently inconsistent experimental 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.
title Implementing Errors on Errors: Bayesian vs Frequentist
topic High Energy Physics - Phenomenology
Instrumentation and Methods for Astrophysics
High Energy Physics - Experiment
Methodology
url https://arxiv.org/abs/2505.06521