# Distributed Gaussian Learning over Time-varying Directed Graphs

arXiv: https://arxiv.org/abs/1612.01600 · HTML: https://mlsanity.com/paper/1612.01600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2016.

## Built on by (most cited first, 1 of 1)

- [Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphs](https://mlsanity.com/paper/2602.20567/) (2026)

## Abstract (arXiv)

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.