Bayesian estimation of non-stationary AR model parameters via an unknown forgetting factor
Anthony Quinn V. Smidl · 2005
We study Bayesian estimation of the time-varying parameters of a non-stationary AR (autoregressive) process. This is traditionally achieved via exponential forgetting. A numerically tractable solution is available if the forgetting factor is known a priori. This assumption is now relaxed. Instead, we propose joint Bayesian estimation of the AR parameters and the unknown forgetting factor. The posterior distribution is intractable, and is approximated using the variational-Bayes (VB) method. Improved parameter tracking is revealed in simulation.