论文标题
通过空间分配并行化MCMC采样
Parallelizing MCMC Sampling via Space Partitioning
论文作者
论文摘要
在许多研究领域中，多维和多模式密度函数的有效采样是引起人们极大兴趣的任务。我们描述了一种算法，该算法允许通过将函数参数的空间划分为多个子空间，并独立采样每个子空间，从而平行固有的序列马尔可夫链蒙特卡洛（MCMC）采样。然后，不同子空间的样品通过其积分值重新恢复并重新缝合在一起。这种方法允许通过并行操作减少采样壁锁定时间。它还改善了多模式目标密度的采样，并导致相关样品较少。最后，该方法得出了目标密度函数积分的估计值。
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.
