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Summary of On Uncertainty-penalized Bayesian Information Criterion, by Pongpisit Thanasutives et al.


On uncertainty-penalized Bayesian information criterion

by Pongpisit Thanasutives, Ken-ichi Fukui

First submitted to arxiv on: 23 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Statistics Theory (math.ST)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The proposed uncertainty-penalized information criterion (UBIC) is used for model selection in data-driven partial differential equation (PDE) discovery. By showing that using UBIC is equivalent to applying conventional BIC to overparameterized models derived from potential regression models, the paper demonstrates that both criteria share similar asymptotic properties.
Low GrooveSquid.com (original content) Low Difficulty Summary
The researchers found a new way to pick the best model for solving PDEs using data. They showed that this approach, called UBIC, is the same as using another method, BIC, but with more complex models. This means that both methods give similar results in the long run.

Keywords

» Artificial intelligence  » Regression