Bayesian fusion: scalable unification of distributed statistical analyses

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.

Towards a Probabilistic Fusion Approach for Robust Battery Prognostics

“…Alternatively, (Bai & Chandra, 2023) described a Bayesian ensemble learning framework that uses gradient boosting by combining multiple Neural Networks trained by Markov Chain Monte Carlo (MCMC) sampling. Finally, (Dai, Pollock, & Roberts, 2023) demonstrate the robustness of Bayesian fusion by embedding the Monte Carlo fusion framework within a sequential Monte Carlo algorithm.…”