PreprintCC BY 4.0arXiv
Quasi-Gaussian Missing Higher Order Uncertainties
Abstract
We review Bayesian models for estimating the uncertainty induced by missing higher orders in perturbative predictions. These models return probability distributions, which have peculiar shapes typically very different from the Gaussian distribution often assumed in physics analysis. In this work, we are able to obtain smooth probability distributions with quasi-Gaussians shapes by suitably modifying the likelihood and the priors of the models.
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BibTeX
@misc{bonvini2026quasigaussian,
title = {{Quasi-Gaussian Missing Higher Order Uncertainties}},
author = {Marco Bonvini and Emanuele Bagnaschi and Lorenzo Paparella},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25824},
eprint = {2609.25824},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25824},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quasi-Gaussian Missing Higher Order Uncertainties
AU - Marco Bonvini
AU - Emanuele Bagnaschi
AU - Lorenzo Paparella
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.25824
UR - https://arxiv.org/abs/2609.25824
AB - We review Bayesian models for estimating the uncertainty induced by missing higher orders in perturbative predictions. These models return probability distributions, which have peculiar shapes typically very different from the Gaussian distribution often assumed in physics analysis. In this work, we are able to obtain smooth probability distributions with quasi-Gaussians shapes by suitably modifying the likelihood and the priors of the models.
ER -