Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output. An ongoing challenge in the literature is to efficiently perform this merging without imposing distributional assumptions on the posteriors. We propose using diffusion generative modelling to fit density approximations to the subposterior distributions. This approach outperforms existing methods on challenging merging problems, while its computational cost scales more efficiently to high dimensional problems than existing density estimation approaches.

翻译：分治马尔可夫链蒙特卡洛是一种通过在不同数据子集上运行独立采样器并合并其输出以实现并行化的策略。当前文献中的一个持续挑战是如何在不强加后验分布假设的前提下高效执行这种合并。我们提出利用扩散生成建模来拟合子后验分布的密度近似。该方法在具有挑战性的合并问题上优于现有方法，同时其计算成本在高维问题中比现有密度估计方法具有更高的可扩展性。