The Population Posterior and Bayesian Modeling on Streams
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.
Cite
Text
McInerney et al. "The Population Posterior and Bayesian Modeling on Streams." Neural Information Processing Systems, 2015.Markdown
[McInerney et al. "The Population Posterior and Bayesian Modeling on Streams." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/mcinerney2015neurips-population/)BibTeX
@inproceedings{mcinerney2015neurips-population,
title = {{The Population Posterior and Bayesian Modeling on Streams}},
author = {McInerney, James and Ranganath, Rajesh and Blei, David},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {1153-1161},
url = {https://mlanthology.org/neurips/2015/mcinerney2015neurips-population/}
}