AR model parameter estimation: from factor graphs to algorithms

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....

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Hauptverfasser:	Korl, S., Loeliger, H.A., Lindgren, A.G.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied sciences Detection, estimation, filtering, equalization, prediction Error correction codes Exact sciences and technology Gaussian noise Graphical models Information processing Information, signal and communications theory Laboratories Parameter estimation Signal and communications theory Signal processing Signal processing algorithms Signal, noise State-space methods Technological innovation Telecommunications and information theory
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Beschreibung
Zusammenfassung:	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.
ISSN:	1520-6149 2379-190X
DOI:	10.1109/ICASSP.2004.1327159