Online Bayesian system identification in multivariate autoregressive models via message passing
This work addresses online uncertainty quantification for system identification in control or signal processing, but it appears incremental as it builds on existing Bayesian and message-passing frameworks.
The authors tackled the problem of online Bayesian system identification in multivariate autoregressive models by proposing a recursive estimation method based on message passing, which produces full posterior distributions for coefficients and noise precision, and demonstrated convergence on a synthetic system and competitive performance on a double mass-spring-damper system.
We propose a recursive Bayesian estimation procedure for multivariate autoregressive models with exogenous inputs based on message passing in a factor graph. Unlike recursive least-squares, our method produces full posterior distributions for both the autoregressive coefficients and noise precision. The uncertainties regarding these estimates propagate into the uncertainties on predictions for future system outputs, and support online model evidence calculations. We demonstrate convergence empirically on a synthetic autoregressive system and competitive performance on a double mass-spring-damper system.