Streaming, distributed variational inference for Bayesian nonparametrics

Streaming, distributed variational inference for Bayesian nonparametrics

This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to...

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Bibliographic Details
Main Authors: Campbell, Trevor David, Straub, Julian, Fisher, John W, How, Jonathan P
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Neural Information Processing Systems Foundation 2016
Online Access:http://hdl.handle.net/1721.1/106134
https://orcid.org/0000-0003-1499-0191
https://orcid.org/0000-0003-2339-1262
https://orcid.org/0000-0003-4844-3495
https://orcid.org/0000-0001-8576-1930

Internet

http://hdl.handle.net/1721.1/106134
https://orcid.org/0000-0003-1499-0191
https://orcid.org/0000-0003-2339-1262
https://orcid.org/0000-0003-4844-3495
https://orcid.org/0000-0001-8576-1930

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