On uncertainty-penalized Bayesian information criterion
Machine Learning
2024-04-29 v1 Statistics Theory
Statistics Theory
Abstract
The uncertainty-penalized information criterion (UBIC) has been proposed as a new model-selection criterion for data-driven partial differential equation (PDE) discovery. In this paper, we show that using the UBIC is equivalent to employing the conventional BIC to a set of overparameterized models derived from the potential regression models of different complexity measures. The result indicates that the asymptotic property of the UBIC and BIC holds indifferently.
Keywords
Cite
@article{arxiv.2404.16881,
title = {On uncertainty-penalized Bayesian information criterion},
author = {Pongpisit Thanasutives and Ken-ichi Fukui},
journal= {arXiv preprint arXiv:2404.16881},
year = {2024}
}
Comments
4 pages, 2 figures