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The Population Posterior and Bayesian Modeling on Streams

Author(s): McInerney, James; Ranganath, Rajesh; Blei, David M

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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.
Publication Date: 2015
Citation: McInerney, James, Rajesh Ranganath, and David Blei. "The Population Posterior and Bayesian Modeling on Streams." Advances in Neural Information Processing Systems 28 (2015), pp. 1153-1161.
ISSN: 1049-5258
Pages: 1153-1161
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Author's manuscript

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