The population posterior and Bayesian modeling on streams

James McInerney, Rajesh Ranganath, David Blei

Research output: Contribution to journalConference article

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 require conditioning 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 develop the population posterior for latent Dirichlet allocation and Dirichlet process mixtures. We study our method with several large-scale data sets.

Original languageEnglish (US)
Pages (from-to)1153-1161
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2015-January
StatePublished - 2015
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    McInerney, J., Ranganath, R., & Blei, D. (2015). The population posterior and Bayesian modeling on streams. Advances in Neural Information Processing Systems, 2015-January, 1153-1161.