TY - JOUR
T1 - The population posterior and Bayesian modeling on streams
AU - McInerney, James
AU - Ranganath, Rajesh
AU - Blei, David
N1 - Funding Information:
We thank Allison Chaney, John Cunningham, Alp Kucukelbir, Stephan Mandt, Peter Orbanz, Theo Weber, Frank Wood, and the anonymous reviewers for their comments. This work is supported by NSF IIS-0745520, IIS-1247664, IIS-1009542, ONR N00014-11-1-0651, DARPA FA8750-14-2-0009, N66001-15-C-4032, NDSEG, Facebook, Adobe, Amazon, and the Siebel Scholar and John Templeton Foundations.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:84965175063
SN - 1049-5258
VL - 2015-January
SP - 1153
EP - 1161
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
T2 - 29th Annual Conference on Neural Information Processing Systems, NIPS 2015
Y2 - 7 December 2015 through 12 December 2015
ER -