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A general model validation and testing tool

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  • Vanslette, Kevin
  • Tohme, Tony
  • Youcef-Toumi, Kamal

Abstract

We construct and propose the “Bayesian Validation Metric†(BVM) as a general model validation and testing tool. We find the BVM to be capable of representing all of the standard validation metrics (square error, reliability, probability of agreement, frequentist, area, probability density comparison, statistical hypothesis testing, and Bayesian model testing) as special cases and find that it can be used to improve, generalize, or further quantify their uncertainties. Thus, the BVM allows us to assess the similarities and differences between existing validation metrics in a new light.

Suggested Citation

  • Vanslette, Kevin & Tohme, Tony & Youcef-Toumi, Kamal, 2020. "A general model validation and testing tool," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019302571
    DOI: 10.1016/j.ress.2019.106684
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    References listed on IDEAS

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    Cited by:

    1. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2023. "Reliable neural networks for regression uncertainty estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Khastgir, Siddartha & Brewerton, Simon & Thomas, John & Jennings, Paul, 2021. "Systems Approach to Creating Test Scenarios for Automated Driving Systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Pietrantuono, Roberto & Popov, Peter & Russo, Stefano, 2020. "Reliability assessment of service-based software under operational profile uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2020. "A generalized Bayesian approach to model calibration," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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