# Continuous Herded Gibbs Sampling

arXiv: https://arxiv.org/abs/2106.06430 · HTML: https://mlsanity.com/paper/2106.06430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering](https://mlsanity.com/paper/1501.02056/) (2015)
- [Herded Gibbs Sampling](https://mlsanity.com/paper/1301.4168/) (2013)

## Abstract (arXiv)

Herding is a technique to sequentially generate deterministic samples from a probability distribution. In this work, we propose a continuous herded Gibbs sampler that combines kernel herding on continuous densities with the Gibbs sampling idea. Our algorithm allows for deterministically sampling from high-dimensional multivariate probability densities, without directly sampling from the joint density. Experiments with Gaussian mixture densities indicate that the L2 error decreases similarly to kernel herding, while the computation time is significantly lower, i.e., linear in the number of dimensions.