Abstract
We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parameters of the Gaussian beliefs (i.e. means and precision). We show a convergence rate of O(1/k) with the constant term depending on the number of agents and the topology of the network. Moreover, we show almost sure convergence to the optimal solution of the estimation problem for the general case of time-varying directed graphs.
| Original language | English (US) |
|---|---|
| Title of host publication | Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 |
| Editors | Michael B. Matthews |
| Publisher | IEEE Computer Society |
| Pages | 1710-1714 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538639542 |
| DOIs | |
| State | Published - Mar 1 2017 |
| Event | 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016 |
Publication series
| Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
|---|---|
| ISSN (Print) | 1058-6393 |
Other
| Other | 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 |
|---|---|
| Country | United States |
| City | Pacific Grove |
| Period | 11/6/16 → 11/9/16 |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
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Cite this
Nedich, A., Olshevsky, A., & Uribe, C. A. (2017). Distributed Gaussian learning over time-varying directed graphs. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 1710-1714). [7869674] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869674