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
