The Population Posterior and Bayesian Inference on Streams

TL;DR

This paper introduces the population variational Bayes method for Bayesian inference on streaming data, enabling probabilistic modeling that accounts for data arriving continuously, demonstrated on large-scale datasets.

Contribution

It proposes the population variational Bayes approach, a novel method for Bayesian inference on data streams that approximates the population posterior distribution.

Findings

Effective on large-scale datasets

Applicable to latent Dirichlet allocation and Dirichlet process mixtures

Enables Bayesian analysis of streaming data

Abstract

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.