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Computer Science > Machine Learning
Title: On uncertainty-penalized Bayesian information criterion
(Submitted on 23 Apr 2024)
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.
Submission history
From: Pongpisit Thanasutives [view email][v1] Tue, 23 Apr 2024 13:59:11 GMT (307kb,D)
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