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International Federation of Automatic Control (IFAC), IFAC papers online, 16(45), p. 203-208, 2012

DOI: 10.3182/20120711-3-be-2027.00297

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Estimation of Linear Systems using a Gibbs Sampler

Journal article published in 2012 by Adrian Wills, Thomas B. Schön ORCID, Fredrik Lindsten, Brett Ninness
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

This paper considers a Bayesian approach to linear system identification. One motivation is the advantage of the minimum mean square error of the associated conditional mean estimate. A further motivation is the error quantifications afforded by the posterior density which are not reliant on asymptotic in data length derivations. To compute these posterior quantities, this paper derives and illustrates a Gibbs sampling approach, which is a randomized algorithm in the family of Markov chain Monte Carlo methods. We provide details on a numerically robust implementation of the Gibbs sampler. In a numerical example, the proposed method is illustrated to give good convergence properties without requiring any user tuning.