NeurIPS 2017poster12 citations
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
Abstract
We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution. Similarly to Hogwild methods, our approach does not target the original Gaussian distribution of interest, but an approximation to it. Contrary to Hogwild methods, a single parameter allows us to trade bias for variance. We show empirically that our method is very flexible and performs well compared to Hogwild-type algorithms.
BibTeX
@inproceedings{NIPS2017_7876acb6,
author = {Barbos, Andrei-Cristian and Caron, Francois and Giovannelli, Jean-Fran\c{c}ois and Doucet, Arnaud},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/7876acb66640bad41f1e1371ef30c180-Paper.pdf},
volume = {30},
year = {2017}
}