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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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dc.contributor.authorMcInerney, James-
dc.contributor.authorRanganath, Rajesh-
dc.contributor.authorBlei, David M-
dc.date.accessioned2021-10-08T19:44:20Z-
dc.date.available2021-10-08T19:44:20Z-
dc.date.issued2015en_US
dc.identifier.citationMcInerney, 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.en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttp://papers.nips.cc/paper/5793-the-population-posterior-and-bayesian-modeling-on-streams-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1nv67-
dc.description.abstractMany 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.en_US
dc.format.extent1153-1161en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsAuthor's manuscripten_US
dc.titleThe Population Posterior and Bayesian Modeling on Streamsen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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