Ar model parameter estimation: from factor graphs to algorithms (2004)
by
Sascha Korl
,
Hans-andrea Loeliger
,
Eth Zürich
,
Allen G. Lindgren
| Venue: | in Acoustics, Speech, and Signal Processing, 2004. Proceedings. IEEE International Conference |
| Citations: | 4 - 1 self |
BibTeX
@INPROCEEDINGS{Korl04armodel,
author = {Sascha Korl and Hans-andrea Loeliger and Eth Zürich and Allen G. Lindgren},
title = {Ar model parameter estimation: from factor graphs to algorithms},
booktitle = {in Acoustics, Speech, and Signal Processing, 2004. Proceedings. IEEE International Conference},
year = {2004}
}
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Abstract
The classic problem of estimating the parameters of an auto-regressive (AR) model is considered from a graphicalmodel 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 joint estimation of AR coefficients, innovation variance, and noise variance. 1.
Citations
| 767 | Factor graphs and the sum-product algorithm - Kschischang, Frey, et al. - 2001 |
| 663 | Sequential Monte Carlo methods in practice, Statistics for Engineering and Information Sciences - Doucet, Freitas, et al. - 2001 |
| 185 | A family of algorithms for approximate Bayesian inference - Minka - 2001 |
| 67 | An introduction to factor graphs - Loeliger - 2004 |
| 12 | Least squares and Kalman filtering on Forney graphs - Loeliger - 2002 |
