Bayesian system identification via Markov chain Monte Carlo techniques

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

• Type: Journal
• Year: 2010
• Where: AUTOMATICA
• Authors: Brett Ninness, Soren J. Henriksen
• Keywords: AUTOMATICA 2010, Ergodic Markov Chain, Estimation, Required Posterior Density