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Bayesian fusion: Scalable unification of distributed statistical analyses

Lookup NU author(s): Professor Hongsheng DaiORCiD, Dr Murray Pollock

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2023 Blackwell Publishing Ltd. All rights reserved.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.

Publication metadata

Author(s): Dai H, Pollock M, Roberts GO

Publication type: Article

Publication status: Published

Journal: Journal of the Royal Statistical Society. Series B: Statistical Methodology

Year: 2023

Volume: 85

Issue: 1

Pages: 84-107

Print publication date: 01/02/2023

Online publication date: 30/01/2023

Acceptance date: 26/11/2022

Date deposited: 20/03/2023

ISSN (print): 1369-7412

ISSN (electronic): 1467-9868

Publisher: Oxford University Press

URL: https://doi.org/10.1093/jrsssb/qkac007

DOI: 10.1093/jrsssb/qkac007

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