Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. We also provide extensive simulation results to demonstrate the validity of the proposed approach.

中文翻译：

---

协同传感器网络中基于随机梯度的分布式贝叶斯估计

分布式贝叶斯推论提供了不确定性的完整量化，与自主传感器网络能够利用的点估计相比，具有许多优势。但是，完全分散的贝叶斯推理通常需要较大的通信开销和较低的网络延迟，而这些资源在实际应用中通常是不可用的。在本文中，我们提出了一种基于随机梯度Langevin动力学的分散贝叶斯推理方法，该方法在每个节点上产生完整的后验分布，而通信开销却大大降低。在典型的网络约束条件下，我们提供了将所提出的分布式算法收敛到集中式后验算法的分析结果。我们还提供了广泛的仿真结果，以证明所提出方法的有效性。