14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrA2.1

McVinish, Ross (Queensland Univ. of Tech.), Braslavsky, Julio H. (The Univ. of Newcastle), Mengersen, Kerrie (Queensland Univ. of Tech.)

A Bayesian - Decision Theoretic Approach to Model Error Modeling

Scheduled for presentation during the Regular Session "Model Error Quantification and Model Validation" (FrA2), Friday, March 31, 2006, 10:30−10:50, Banquet Room

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

This information is tentative and subject to change. Compiled on September 24, 2018

Keywords Bayesian Methods, Nonparametric Methods


This paper takes a Bayesian-decision theoretic approach to transfer function estimation, nominal model estimation, and quantification of the resulting model error. Consistency of the nonparametric estimate of the transfer function is proved together with a rate of convergence. The required quantities can be computed routinely using reversible jump Markov chain Monte Carlo methods. The proposed methodology has connections with set membership identification which has been extensively studied for this problem.