Interval predictor models and genreralization error bounds

バージョン 1.10 (20.3 MB) 作成者: roberto rocchetta

Different Training Schemes for Interval Predictor Model and Generalization Bounds on the reliability of their predictions

https://github.com/Roberock/ScenarioIPM

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更新 2021/5/4

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An Interval Predictor Model (IPM) offers an interval-valued characterization of the uncertainty affecting a stochastic process.
The reliability of the optimized predictor (probability that future samples will fall outside from the predictive bounds) is formally bounded thanks to scenario theory

引用

roberto rocchetta (2025). Interval predictor models and genreralization error bounds (https://github.com/Roberock/ScenarioIPM), GitHub. October 25、2025に取得済み.

Rocchetta, Roberto, et al. “Soft-Constrained Interval Predictor Models and Epistemic Reliability Intervals: A New Tool for Uncertainty Quantification with Limited Experimental Data.” Mechanical Systems and Signal Processing, vol. 161, Elsevier BV, Dec. 2021, p. 107973, doi:10.1016/j.ymssp.2021.107973.

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@IPM_Model

IPM_Model

IPM_functions

ScenarioBounds

datasets

GitHub の既定のブランチを使用するバージョンはダウンロードできません

バージョン	公開済み	リリースノート
1.10	2021/5/4	included journal paper citation
1.1	2021/4/11	included missing files, include a new example and data
1.0	2021/4/10		ダウンロード

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MLA	Rocchetta, Roberto, et al. “Soft-Constrained Interval Predictor Models and Epistemic Reliability Intervals: A New Tool for Uncertainty Quantification with Limited Experimental Data.” Mechanical Systems and Signal Processing, vol. 161, Elsevier BV, Dec. 2021, p. 107973, doi:10.1016/j.ymssp.2021.107973.
APA	Rocchetta, R., Gao, Q., & Petkovic, M. (2021). Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data. Mechanical Systems and Signal Processing, 161, 107973. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.ymssp.2021.107973
BibTeX	@article{Rocchetta_2021, doi = {10.1016/j.ymssp.2021.107973}, url = {https://doi.org/10.1016%2Fj.ymssp.2021.107973}, year = 2021, month = {dec}, publisher = {Elsevier {BV}}, volume = {161}, pages = {107973}, author = {Roberto Rocchetta and Qi Gao and Milan Petkovic}, title = {Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data}, journal = {Mechanical Systems and Signal Processing} }