Journal of Verification, Validation and Uncertainty Quantification. 'Interval Predictor Models With a Linear Parameter Dependency'. 'Staircase predictor models for reliability and risk analysis'.

'Interval predictor models: Identification and reliability'. OpenCOSSAN provides a Matlab implementation of the work of Crespo (2015). PyIPM provides an open-source Python implementation of the work of Crespo (2015). Hence the IPM can be trained with a linear optimization program:Ī r g m i n p ⁡ -regression) belong to a particular class of Interval Predictor Models, for which the reliability is invariant with respect to the distribution of the data. Ĭrespo (2016) proposed the use of a hyperrectangular parameter set, which results in a convenient, linear form for the bounds of the IPM. Then a convex set is assigned to the parameter vector, and the size of the convex set is minimized such that every possible data point can be predicted by one possible value of the parameters.Įllipsoidal parameters sets were used by Campi (2009), which yield a convex optimization program to train the IPM. Usually the basis is made up of polynomial features or a radial basis is sometimes used. Typically the interval predictor model is created by specifying a parametric function, which is usually chosen to be the product of a parameter vector and a basis.

P1612 loss of ipm serial data