14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrA2.2

Douma, Sippe G. (SHELL), Van den Hof, Paul M.J. (Delft Univ. of Tech.)

Probabilistic Model Uncertainty Bounding: An Approach with Finite-Time Perspectives

Scheduled for presentation during the Regular Session "Model Error Quantification and Model Validation" (FrA2), Friday, March 31, 2006, 10:50−11:10, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 16, 2018

Keywords Error Quantification, Model Validation, Basis Functions


In prediction error identification model uncertainty bounds are generally derived from the statistical properties of the parameter estimator, i.e. asymptotic normal distribution of the estimator, and availability of the covariance information. When the primal interest of the identification is in a-posteriori quantifying the uncertainty in an estimated parameter, alternative parameter confidence bounds can be constructed. Probabilistic parameter confidence bounds are studied for ARX models which are generated by computationally more simple expressions, and which have the potential of being less dependent on asymptotic approximations and assumptions. It is illustrated that the alternative bounds can be powerful for quantifying parameter confidence regions for finite-time situations.