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| Paper: | MLSP-L3.3 |
| Session: | Learning Theory and Modeling |
| Time: | Friday, May 21, 16:10 - 16:30 |
| Presentation: |
Lecture |
| Topic: |
Machine Learning for Signal Processing: Bayesian Learning and Modeling |
| Title: |
AR MODEL PARAMETER ESTIMATION: FROM FACTOR GRAPHS TO ALGORITHMS |
| Authors: |
Sascha Korl; ETH Zürich | | |
| | Hans-Andrea Loeliger; ETH Zürich | | |
| | Allen Lindgren; University of Rhode Island | | |
| Abstract: |
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---most of them well-known, some 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. |
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