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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| creator | Korl, S. Loeliger, H.A. Lindgren, A.G. |
| description | 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. |
| doi_str_mv | 10.1109/ICASSP.2004.1327159 |
| format | Conference Proceeding |
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| ispartof | 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.5, p.V-509 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_1327159 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | 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 |
| title | AR model parameter estimation: from factor graphs to algorithms |
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