Structured variational methods for distributed inference in wireless ad hoc and sensor networks

In this paper, a variational message passing framework is proposed for Markov random fields, which is computationally more efficient and admits wider applicability compared to the belief propagation algorithm. 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 accuracy of exact inference (for intra-cluster processing). Its performance is elaborated on a Gaussian Markov random field, through both theoretical analysis and simulation results.