Interval predictor models and genreralization error bounds

Versione 1.10 (20,3 MB) da 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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Aggiornato 4 mag 2021

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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

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roberto rocchetta (2024). Interval predictor models and genreralization error bounds (https://github.com/Roberock/ScenarioIPM), GitHub. Recuperato aprile 26, 2024.

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

Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione	Pubblicato	Note della release
1.10	4 mag 2021	included journal paper citation
1.1	11 apr 2021	included missing files, include a new example and data
1.0	10 apr 2021		Scarica

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.

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} }