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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

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Updated 04 May 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

Cite As

roberto rocchetta (2021). 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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MATLAB Release Compatibility
Created with R2021a
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