# Streaming Variational Inference for Bayesian Nonparametric Mixture Models

arXiv: https://arxiv.org/abs/1412.0694 · HTML: https://mlsanity.com/paper/1412.0694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2014.

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

- [Streaming Inference for Infinite Non-Stationary Clustering](https://mlsanity.com/paper/2205.01212/) (2022)

## Abstract (arXiv)

In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data. Unfortunately, such benefits have not been fully realized in practice; existing inference algorithms are either not applicable to streaming applications or not extensible to BNP models. For the special case of Dirichlet processes, streaming inference has been considered. However, there is growing interest in more flexible BNP models building on the class of normalized random measures (NRMs). We work within this general framework and present a streaming variational inference algorithm for NRM mixture models. Our algorithm is based on assumed density filtering (ADF), leading straightforwardly to expectation propagation (EP) for large-scale batch inference as well. We demonstrate the efficacy of the algorithm on clustering documents in large, streaming text corpora.