On uncertainty-penalized Bayesian information criterion
CoRR(2024)
摘要
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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