On uncertainty-penalized Bayesian information criterion

Pongpisit Thanasutives, Ken-ichi Fukui
4/23/2024
cs.LGmath.ST

No official code found. Best implementation: Pongpisit-Thanasutives/UBIC-play (1 star, MIT, last commit 2025-05-04). 1 more below.

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.

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Code Implementations(2)

Pongpisit-Thanasutives/UBIC-playCommunity73%
MIT

Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery

10Python, Jupyter NotebookFeb 17, 20241 years agoMIT
bayesian-information-criterioninformation-criterionmodel-discoverymodel-selectionpartial-differential-equations+3 more
Pongpisit-Thanasutives/UBIC-testCommunity71%
MIT

Uncertainty-penalized Bayesian information criterion (UBIC) for PDE Discovery

00Python, Jupyter NotebookMar 2, 20241 years agoMIT
bayesian-information-criterioninformation-criterionmodel-discoverymodel-selectionpartial-differential-equations+3 more

Cite this paper

@article{thanasutives2024uncertaintypenalized,
  title  = {On uncertainty-penalized Bayesian information criterion},
  author = {Pongpisit Thanasutives and Ken-ichi Fukui},
  year   = {2024},
  eprint = {2404.16881},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.16881v1}
}

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