This paper compares classical parametric methods with recently developed regularization/Bayesian methods for system identification. A Full Bayes solution is considered together with the approximation based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for confidence regions are reported.
Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets
PRANDO, GIULIA;ROMERES, DIEGO;PILLONETTO, GIANLUIGI;CHIUSO, ALESSANDRO
2016
Abstract
This paper compares classical parametric methods with recently developed regularization/Bayesian methods for system identification. A Full Bayes solution is considered together with the approximation based on the Empirical Bayes paradigm. Results regarding point estimators for the impulse response as well as for confidence regions are reported.
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