Mathematical Programs for Belief Propagation and Consensus
It provides a framework for distributed inference in multi-agent systems, but the results are presented without concrete performance numbers, making the significance unclear.
The paper develops distributed Bayesian hypothesis testing methods for fault detection and diagnosis using belief propagation and optimization, addressing challenges in convergence, scalability, and communication constraints in networked multi-agent systems.
This paper develops methods of distributed Bayesian hypothesis tests for fault detection and diagnosis that are based on belief propagation and optimization in graphical models. The main challenges in developing distributed statistical estimation algorithms are i) difficulties in ensuring convergence and consensus for solutions of distributed inference problems, ii) increasing computational costs due to lack of scalability, and iii) communication constraints for networked multi-agent systems. To cope with those challenges, this manuscript considers i) belief propagation and optimization in graphical models of complex distributed systems, ii) decomposition methods of optimization for parallel and iterative computations, and iii) distributed decision-making protocols.