PreprintCC BY-NC-ND 4.0HAL
Binning-Independent Bayesian Analysis of Time-Dependent Perturbed Angular Distribution data
Abstract
We propose a new approach for analyzing Time-Dependent Perturbed Angular Distribution data. In this, a likelihood is constructed with the help of conditional probabilities of single events and binning of time data is avoided. This likelihood is used in a Bayesian framework to calculate the posterior probability density function for the g factor of interest. This approach is compared to more traditional approaches that use binned data by analyzing simulated datasets. In many cases the resulting posterior probability densities are observed to be multimodal and the results can thus often not be summarized with a single Gaussian approximation. We find that results from the new approach are more reliable for low-statistics datasets.
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BibTeX
@misc{spee2026binningindependent,
title = {{Binning-Independent Bayesian Analysis of Time-Dependent Perturbed Angular Distribution data}},
author = {Franziskus von Spee},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.19227},
eprint = {2609.19227},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05763121v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Binning-Independent Bayesian Analysis of Time-Dependent Perturbed Angular Distribution data
AU - Franziskus von Spee
PY - 2026
DA - 2026/09/16
PB - HAL
DO - 10.48550/arxiv.2609.19227
UR - https://hal.science/hal-05763121v1
AB - We propose a new approach for analyzing Time-Dependent Perturbed Angular Distribution data. In this, a likelihood is constructed with the help of conditional probabilities of single events and binning of time data is avoided. This likelihood is used in a Bayesian framework to calculate the posterior probability density function for the g factor of interest. This approach is compared to more traditional approaches that use binned data by analyzing simulated datasets. In many cases the resulting posterior probability densities are observed to be multimodal and the results can thus often not be summarized with a single Gaussian approximation. We find that results from the new approach are more reliable for low-statistics datasets.
ER -