  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.
