Streaming Variational Bayes
Abstract
We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior. The framework makes streaming updates to the estimated posterior according to a user-specified approximation primitive function. We demonstrate the usefulness of our framework, with variational Bayes (VB) as the primitive, by fitting the latent Dirichlet allocation model to two large-scale document collections. We demonstrate the advantages of our algorithm over stochastic variational inference (SVI), both in the single-pass setting SVI was designed for and in the streaming setting, to which SVI does not apply.
Cite
Text
Broderick et al. "Streaming Variational Bayes." Neural Information Processing Systems, 2013.Markdown
[Broderick et al. "Streaming Variational Bayes." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/broderick2013neurips-streaming/)BibTeX
@inproceedings{broderick2013neurips-streaming,
title = {{Streaming Variational Bayes}},
author = {Broderick, Tamara and Boyd, Nicholas and Wibisono, Andre and Wilson, Ashia C and Jordan, Michael I},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {1727-1735},
url = {https://mlanthology.org/neurips/2013/broderick2013neurips-streaming/}
}