Bayesian fusion: scalable unification of distributed statistical analyses

• 成果类型: Article
• 署名作者: Dai, Hongsheng; Pollock, Murray; Roberts, Gareth O.
• 署名单位: University of Essex; Newcastle University - UK; Alan Turing Institute; University of Warwick
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkac007
• 发表日期: 2023
• 页码: 84-107
• 关键词: gibbs sampler; inference; algorithms; simulation

摘要

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.