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Bayesian error isolation for models of large-scale systems

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1 Author(s)
J. C. Spall ; Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA

A methodology is presented for use in isolating sources of misspecification in system models that are known to be invalid. The methodology relies on a technique based on stochastic approximation in the context of a Bayesian formulation. This approach has significant advantages in computational efficiency, relative to a straightforward Bayesian analysis, for large-scale systems. Moreover, it applies to arbitrary model forms (e.g. state-space, regression, etc.) and applies when the probability distribution for the system output is not necessarily Gaussian

Published in:

IEEE Transactions on Automatic Control  (Volume:33 ,  Issue: 4 )