@inproceedings{9247f0b8384c44fdb1b5845e8a18211c,
title = "Distributed Gaussian learning over time-varying directed graphs",
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.",
author = "Angelia Nedich and Alex Olshevsky and Uribe, {Cesar A.}",
year = "2017",
month = mar,
day = "1",
doi = "10.1109/ACSSC.2016.7869674",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1710--1714",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016",
note = "50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 ; Conference date: 06-11-2016 Through 09-11-2016",
}