# Streaming, Distributed Variational Inference for Bayesian Nonparametrics

arXiv: https://arxiv.org/abs/1510.09161 · HTML: https://mlsanity.com/paper/1510.09161/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2015.

## Built on by (most cited first, 5 of 5)

- [Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data](https://mlsanity.com/paper/2202.13312/) (2022)
- [Online Learning of a Probabilistic and Adaptive Scene Representation](https://mlsanity.com/paper/2103.16832/) (2021)
- [Streaming Inference for Infinite Non-Stationary Clustering](https://mlsanity.com/paper/2205.01212/) (2022)
- [Federated Variational Inference for Bayesian Mixture Models](https://mlsanity.com/paper/2502.12684/) (2025)
- [Dirichlet process mixture models for non-stationary data streams](https://mlsanity.com/paper/2210.06872/) (2022)

## Abstract (arXiv)

This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In contrast to previous algorithms, the proposed framework is truly streaming, distributed, asynchronous, learning-rate-free, and truncation-free. The key challenge in developing the framework, arising from the fact that BNP models do not impose an inherent ordering on their components, is finding the correspondence between minibatch and central BNP posterior components before performing each update. To address this, the paper develops a combinatorial optimization problem over component correspondences, and provides an efficient solution technique. The paper concludes with an application of the methodology to the DP mixture model, with experimental results demonstrating its practical scalability and performance.