Efficient sampling of many-dimensional and multimodal density functions is a task of great interest in many research fields. We describe an algorithm that allows parallelizing inherently serial Markov chain Monte Carlo (MCMC) sampling by partitioning the space of the function parameters into multiple subspaces and sampling each of them independently. The samples of the different subspaces are then reweighted by their integral values and stitched back together. This approach allows reducing sampling wall-clock time by parallel operation. It also improves sampling of multimodal target densities and results in less correlated samples. Finally, the approach yields an estimate of the integral of the target density function.

多维和多模态密度函数的有效采样是许多研究领域非常感兴趣的任务。我们描述了一种算法，该算法通过将函数参数的空间划分为多个子空间并独立地对每个子空间进行采样，从而允许并行化固有的串行马尔可夫链蒙特卡罗 (MCMC) 采样。然后，不同子空间的样本通过它们的积分值重新加权并重新缝合在一起。这种方法允许通过并行操作减少采样挂钟时间。它还改进了多模式目标密度的采样，并导致相关性较低的样本。最后，该方法产生了目标密度函数积分的估计。