AR model parameter estimation: from factor graphs to algorithms
S. Korl, Hans‐Andrea Loeliger, A.G. Lindgren · 2004
The classic problem of estimating the parameters of an auto-regressive (AR) model is considered from a graphical model viewpoint. A number of practical parameter estimation algorithms - some of them well known, others apparently new - are derived as "summary propagation" in a factor graph. In particular, we demonstrate the joint estimation of AR coefficients, innovation variance, and noise variance.