Approximate Slice Sampling for Bayesian Posterior Inference
Abstract
In this paper, we advance the theory of large scale Bayesian posterior inference by introducing a new approximate slice sampler that uses only small mini-batches of data in every iteration. While this introduces a bias in the stationary distribution, the computational savings allow us to draw more samples in a given amount of time and reduce sampling variance. We empirically verify on three different models that the approximate slice sampling algorithm can significantly outperform a traditional slice sampler if we are allowed only a fixed amount of computing time for our simulations.
Cite
Text
DuBois et al. "Approximate Slice Sampling for Bayesian Posterior Inference." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[DuBois et al. "Approximate Slice Sampling for Bayesian Posterior Inference." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/dubois2014aistats-approximate/)BibTeX
@inproceedings{dubois2014aistats-approximate,
title = {{Approximate Slice Sampling for Bayesian Posterior Inference}},
author = {DuBois, Christopher and Balan, Anoop Korattikara and Welling, Max and Smyth, Padhraic},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {185-193},
url = {https://mlanthology.org/aistats/2014/dubois2014aistats-approximate/}
}