# D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM

arXiv: https://arxiv.org/abs/1507.00824 · HTML: https://mlsanity.com/paper/1507.00824/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2015.

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

- [Federated ADMM from Bayesian Duality](https://mlsanity.com/paper/2506.13150/) (2025)

## Abstract (arXiv)

Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed Matrix Factorization (MF) and distributed affine structure from motion (SfM).