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Interval Predictor Models are sometimes referred to as a [[nonparametric regression]] technique, because a potentially infinite set of functions are contained by the IPM, and no specific distribution is implied for the regressed variables.
Multiple-input multiple-output IPMs for multi-point data commonly used to represent functions have been recently developed.<ref>{{cite book | doi=10.1109/CDC45484.2021.9683582 | chapter=Interval Predictor Models for Robust System Identification | title=2021 60th IEEE Conference on Decision and Control (CDC) | year=2021 | last1=Crespo | first1=Luis G. | last2=Kenny | first2=Sean P. | last3=Colbert | first3=Brendon K. | last4=Slagel | first4=Tanner | pages=872–879 | isbn=978-1-6654-3659-5 | s2cid=246479771 }}</ref> These IPM prescribe the parameters of the model as a path-connected, semi-algebraic set using sliced-normal <ref>{{cite journal |last1=Crespo |first1=Luis |last2=Colbert |first2=Brendon |last3=Kenny |first3=Sean |last4=Giesy |first4=Daniel |title=On the quantification of aleatory and epistemic uncertainty using Sliced-Normal distributions |journal=Systems and Control Letters |date=2019 |volume=34 |page=104560 |doi=10.1016/j.sysconle.2019.104560 |s2cid=209339118 |url=https://doi.org/10.1016/j.sysconle.2019.104560|url-access=subscription }}</ref> or sliced-exponential distributions.<ref>{{cite book | doi=10.1109/CDC45484.2021.9683584 | chapter=Robust Estimation of Sliced-Exponential Distributions<sup>⋆</sup> | title=2021 60th IEEE Conference on Decision and Control (CDC) | year=2021 | last1=Crespo | first1=Luis G. | last2=Colbert | first2=Brendon K. | last3=Slager | first3=Tanner | last4=Kenny | first4=Sean P. | pages=6742–6748 | isbn=978-1-6654-3659-5 | s2cid=246476974 }}</ref> A key advantage of this approach is its ability to characterize complex parameter dependencies to varying fidelity levels. This practice enables the analyst to adjust the desired level of conservatism in the prediction.
As a consequence of the theory of [[scenario optimization]], in many cases rigorous predictions can be made regarding the performance of the model at test time.<ref name="CampiCalafiore2009">{{cite journal|last1=Campi|first1=M.C.|last2=Calafiore|first2=G.|last3=Garatti|first3=S.|title=Interval predictor models: Identification and reliability|journal=Automatica|volume=45|issue=2|year=2009|pages=382–392|issn=0005-1098|doi=10.1016/j.automatica.2008.09.004}}</ref>
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