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.

翻译：放牧是一种根据概率分布顺序生成确定性样本的技术。 在这项工作中, 我们提议一个连续的群群Gibbs取样器, 将连续密度的内圈放牧与Gibbs取样想法结合起来。 我们的算法允许从高维多变概率密度中进行确定性取样, 而不直接从联合密度中取样。 与高斯混合密度的实验表明, L2 误差与内圈放牧相似, 而计算时间则要大大低得多, 也就是说, 尺寸数线性 。