On uncertainty-penalized Bayesian information criterion
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916223946588160 |
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| author | Thanasutives, Pongpisit Fukui, Ken-ichi |
| author_facet | Thanasutives, Pongpisit Fukui, Ken-ichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On uncertainty-penalized Bayesian information criterion Thanasutives, Pongpisit Fukui, Ken-ichi Machine Learning Statistics Theory 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. |
| title | On uncertainty-penalized Bayesian information criterion |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2404.16881 |