| 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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