Bayesian fusion: scalable unification of distributed statistical analyses
提出一种基于序贯蒙特卡洛的贝叶斯融合方法，用于将多个分布式分析结果统一为单一推断，相比现有方法更稳健且可扩展。
Abstract There has been considerable interest in addressing the problem of unifying distributed analyses into a single coherent inference, which arises in big-data settings, when working under privacy constraints, and in Bayesian model choice. Most existing approaches relied upon approximations of the distributed analyses, which have significant shortcomings—the quality of the inference can degrade rapidly with the number of analyses being unified, and can be substantially biased when unifying analyses that do not concur. In contrast, recent Monte Carlo fusion approach is exact and based on rejection sampling. In this paper, we introduce a practical Bayesian fusion approach by embedding the Monte Carlo fusion framework within a sequential Monte Carlo algorithm. We demonstrate theoretically and empirically that Bayesian fusion is more robust than existing methods.