Structured Variational Methods for Distributed Inference in Networked Systems: Design and Analysis

In this paper, a variational message passing framework is proposed for distributed inference in networked systems. Based on this framework, structured variational methods are explored to take advantage of both the simplicity of variational approximation (for inter-cluster processing) and the quality of more accurate inference (for intra-cluster processing). To investigate the convergence performance of our inference approach, we distinguish the inter- and intra-cluster inference algorithms as vertex and edge processes, respectively. Based on an analysis on the intracluster inference procedure, the overall performance of structured variational methods, modeled as a mixed vertex-edge process, is quantitatively characterized via a coupling approach. The tradeoff between performance and complexity of this inference approach is also addressed.