Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
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.
Cite
Text
Barbos et al. "Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling." Neural Information Processing Systems, 2017.Markdown
[Barbos et al. "Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/barbos2017neurips-clone/)BibTeX
@inproceedings{barbos2017neurips-clone,
title = {{Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling}},
author = {Barbos, Andrei-Cristian and Caron, Francois and Giovannelli, Jean-François and Doucet, Arnaud},
booktitle = {Neural Information Processing Systems},
year = {2017},
pages = {5020-5028},
url = {https://mlanthology.org/neurips/2017/barbos2017neurips-clone/}
}