Off-topic for aiqbench
This story was gathered from a source we follow but falls outside AI and quantum coverage, so there is no aiqbench take for it.
Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process
arXiv:2610.21136v1 Announce Type: cross Abstract: We characterize the multiplicative half-Cauchy process through its log-scale random-walk representation, establishing shrinkage, expansion, and critical regimes, almost-sure summability, local rebounds, and effective dimension. We also derive a semi-analytic calibration rule linking interpretable prior probability statements to its scale parameter.
Abstract:We characterize the multiplicative half-Cauchy process through its log-scale random-walk representation, establishing shrinkage, expansion, and critical regimes, almost-sure summability, local rebounds, and effective dimension. We also derive a semi-analytic calibration rule linking interpretable prior probability statements to its scale parameter.
6 pages, 1 figure, 1 table. R package: this https URL. Software DOI: https://doi.org/10.5281/zenodo.23355811
Methodology (stat.ME); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
arXiv:2610.21136 [stat.ME]
(or arXiv:2610.21136v1 [stat.ME] for this version)
https://doi.org/10.48550/arXiv.2610.21136 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 155 words
Related coverage

Grantmakers: Consider sharing rough probabilities & brief feedback with grantees/applicants
LessWrong · Sep 21, 2026
[AINews] High Taste Testers Ep. 1
Latent Space · Oct 17, 2026
Does preservation make sense before we know how to revive?
LessWrong · Jun 15, 2026

The Entangled Dimensions of Decision Theory
LessWrong · Jul 30, 2026