Interval predictor models and genreralization error bounds

Version 1.10 (20.3 MB) by roberto rocchetta

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

https://github.com/Roberock/ScenarioIPM

0.0

(0)

65 Downloads

Updated 4 May 2021

View License on GitHub

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

Cite As

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

View more styles

MATLAB Release Compatibility

Created with R2021a

Compatible with any release

Platform Compatibility

Windows macOS Linux

Tags Add Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

@IPM_Model

IPM_Model

IPM_functions

ScenarioBounds

datasets

Versions that use the GitHub default branch cannot be downloaded

Version	Published	Release Notes
1.10	4 May 2021	included journal paper citation
1.1	11 Apr 2021	included missing files, include a new example and data
1.0	10 Apr 2021		Download

To view or report issues in this GitHub add-on, visit the 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} }