Frequentist Guarantees of Distributed (Non)-Bayesian Inference

arXiv:2311.08214v2 Announce Type: replace-cross
Abstract: Motivated by the need to analyze large, decentralized datasets, distributed Bayesian inference has become a critical research area across multiple fields, including statistics, electrical engineering, and economics. This paper establishes Frequentist properties, such as posterior consistency, asymptotic normality, and posterior contraction rates, for the distributed (non-)Bayes Inference problem among agents connected via a communication network. Our results show that, under appropriate assumptions on the communication graph, distributed Bayesian inference retains parametric efficiency while enhancing robustness …

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