Estimation of Linear Systems using a Gibbs Sampler
2012 (English)In: Proceedings of the 16th IFAC Symposium on System Identification, 2012, 203-208 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2012. 203-208 p.
Parameter estimation, System identification, Bayesian statistics, Markov Chain Monte Carlo techniques
IdentifiersURN: urn:nbn:se:liu:diva-88609DOI: 10.3182/20120711-3-BE-2027.00297ISBN: 978-3-902823-06-9OAI: oai:DiVA.org:liu-88609DiVA: diva2:605130
16th IFAC Symposium on System Identification, Brussels, Belgium, 11-13 July, 2012
FunderSwedish Research Council