Interval predictor models and genreralization error bounds
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 .
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_functions
ScenarioBounds
datasets
Versions that use the GitHub default branch cannot be downloaded
Version | Published | Release Notes | |
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1.10 | included journal paper citation |
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1.1 | included missing files,
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1.0 |