Bayesian system identification via Markov chain Monte Carlo techniques

Title: Bayesian system identification via Markov chain Monte Carlo techniques
Creator: Ninness, Brett; Henriksen, Soren
Relation: Automatica Vol. 46, Issue 1, p. 40-51
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2009.10.015
Publisher: Elsevier
Resource Type: journal article
Date: 2010
Description: The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing accurate quantification of estimation error that is valid for arbitrary, and hence even very short length data records. The main innovation is the employment of the Metropolis–Hastings algorithm to construct an ergodic Markov chain with invariant density equal to the required posterior density. Monte Carlo analysis of samples from this chain then provides a means for efficiently and accurately computing posteriors for model parameters and arbitrary functions of them.
Subject: parameter estimation; system identification; Bayesian methods; maximum likelihood
Identifier: http://hdl.handle.net/1959.13/929380
Identifier: uon:10575
Identifier: ISSN:0005-1098
Language: eng
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