In this article, two novel distributed variational Bayesian (VB) algorithms for a general class of conjugate-exponential models are proposed over synchronous and asynchronous sensor networks. First, we design a penalty-based distributed VB (PB-DVB) algorithm for synchronous networks, where a penalty function based on the Kullback–Leibler (KL) divergence is introduced to penalize the difference of posterior distributions between nodes. Then, a token-passing-based distributed VB (TPB-DVB) algorithm is developed for asynchronous networks by borrowing the token-passing approach and the stochastic variational inference. Finally, applications of the proposed algorithm on the Gaussian mixture model (GMM) are exhibited. Simulation results show that the PB-DVB algorithm has good performance in the aspects of estimation/inference ability, robustness against initialization, and convergence speed, and the TPB-DVB algorithm is superior to existing token-passing-based distributed clustering algorithms.

中文翻译：

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基于传感器网络的分布式贝叶斯推理

在本文中，针对同步和异步传感器网络提出了用于一般类共轭指数模型的两种新型分布式变分贝叶斯 (VB) 算法。首先，我们为同步网络设计了一种基于惩罚的分布式 VB (PB-DVB) 算法，其中引入了基于 Kullback–Leibler (KL) 散度的惩罚函数来惩罚节点之间后验分布的差异。然后，通过借鉴令牌传递方法和随机变分推理，为异步网络开发了基于令牌传递的分布式 VB (TPB-DVB) 算法。最后，展示了该算法在高斯混合模型（GMM）上的应用。仿真结果表明，PB-DVB算法在估计/推理能力方面具有良好的性能，