Interval predictor models and genreralization error bounds

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

https://github.com/Roberock/ScenarioIPM

Version 1.10 (20.3 MB)
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4 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 (2026). 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

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
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Version Published Release Notes
1.10

included journal paper citation
1.1

included missing files,
include a new example and data
1.0

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