| Paper: | MLSP-P2.8 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Bounds on performance | ||
| Title: | A Numerical Method to Compute Cramér-Rao-Type Bounds for Challenging Estimation Problems | ||
| Authors: | Justin Dauwels, RIKEN Brain Science Institute, Japan; Sascha Korl, Phonak AG, Switzerland | ||
| Abstract: | A numerical algorithm is proposed to compute Cramér-Rao-type bounds. The Cramér-Rao-type bounds are derived from information matrices of marginals of the joint pdf of the system at hand. The key ingredient is message-passing on a factor graph of the system. The method can be applied to a wide class of estimation problems. As an illustration, the problem of estimating the parameters of an AR model is considered. | ||
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