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 (Contributor), Straub, Julian (Contributor), Fisher, John W (Contributor), How, Jonathan P (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Neural Information Processing Systems Foundation, 2016-12-22T21:23:37Z.
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