Paper ID: 2404.16881

On uncertainty-penalized Bayesian information criterion

Pongpisit Thanasutives, Ken-ichi Fukui

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.

Submitted: Apr 23, 2024