Stochastic Structured Variational Inference
Abstract
Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization. The algorithm relies on the use of fully factorized variational distributions. However, this “mean-field” independence approximation limits the fidelity of the posterior approximation, and introduces local optima. We show how to relax the mean-field approximation to allow arbitrary dependencies between global parameters and local hidden variables, producing better parameter estimates by reducing bias, sensitivity to local optima, and sensitivity to hyperparameters.
Cite
Text
Hoffman and Blei. "Stochastic Structured Variational Inference." International Conference on Artificial Intelligence and Statistics, 2015.Markdown
[Hoffman and Blei. "Stochastic Structured Variational Inference." International Conference on Artificial Intelligence and Statistics, 2015.](https://mlanthology.org/aistats/2015/hoffman2015aistats-stochastic/)BibTeX
@inproceedings{hoffman2015aistats-stochastic,
title = {{Stochastic Structured Variational Inference}},
author = {Hoffman, Matthew D. and Blei, David M.},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2015},
url = {https://mlanthology.org/aistats/2015/hoffman2015aistats-stochastic/}
}