On uncertainty-penalized Bayesian information criterion
<|reference_start|>On uncertainty-penalized Bayesian information criterion: 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.<|reference_end|>
@article{thanasutives2024on,
    title={On uncertainty-penalized Bayesian information criterion},
    author={Pongpisit Thanasutives, Ken-ichi Fukui},
    journal={arXiv preprint arXiv:2404.16881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16881},
    primaryClass={cs.LG math.ST stat.TH}
}