  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.
