Bayesian fusion: scalable unification of distributed statistical analyses
Bayesian fusion: scalable unification of distributed statistical analyses
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贝叶斯融合：分布式统计分析的可扩展统一
DOI：
10.1093/jrsssb/qkac007
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发表时间：
2023
影响因子：
--
通讯作者：
Dai H
中科院分区：
文献类型：
--
作者：
Dai H
人们对解决将分布式分析统一为单个连贯推理的问题非常感兴趣，这在大数据环境中，在隐私约束下工作时以及贝叶斯模型选择中出现。大多数现有的方法依赖于近似的分布式分析，这有显着的缺点，推理的质量可以迅速降低与分析的数量被统一，并可以大大偏时，统一的分析，不同意。相比之下，最近的蒙特卡罗融合方法是精确的，并基于拒绝采样。在本文中，我们介绍了一个实用的贝叶斯融合方法，通过嵌入蒙特卡罗融合框架内的顺序蒙特卡罗算法。我们从理论和经验上证明，贝叶斯融合比现有的方法更强大。
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.