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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| 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 |