# The Population Posterior and Bayesian Inference on Streams

arXiv: https://arxiv.org/abs/1507.05253 · HTML: https://mlsanity.com/paper/1507.05253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2015.

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

- [Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents](https://mlsanity.com/paper/2606.08552/) (2026)

## Abstract (arXiv)

Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which assume that we condition on finite data. We develop population variational Bayes, a new approach for using Bayesian modeling to analyze streams of data. It approximates a new type of distribution, the population posterior, which combines the notion of a population distribution of the data with Bayesian inference in a probabilistic model. We study our method with latent Dirichlet allocation and Dirichlet process mixtures on several large-scale data sets.