Estimation of linear systems using a Gibbs sampler

Title: Estimation of linear systems using a Gibbs sampler
Creator: Wills, Adrian; Schön, Thomas B.; Lindsten, Fredrik; Ninness, Brett
Relation: 16th IFAC Symposium on System Identification 2012. Proceedings of IFAC 2012: 16th IFAC Symposium on System Identification (Bruxelles, Belgium 11-13 July, 2012) p. 203-208
Publisher Link: http://dx.doi.org/10.3182/20120711-3-BE-2027.00297
Publisher: IFAC
Resource Type: conference paper
Date: 2012
Description: 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.
Subject: parameter estimation; system identification; Bayesian statistics; Markov Chain Monte Carlo techniques
Identifier: http://hdl.handle.net/1959.13/1309121
Identifier: uon:21776
Identifier: ISBN:9783902823069
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format