Mathematical Programs for Belief Propagation and Consensus

TL;DR

This paper introduces distributed Bayesian hypothesis testing methods using belief propagation and optimization in graphical models to improve fault detection and diagnosis in networked systems.

Contribution

It presents novel distributed inference algorithms that address convergence, scalability, and communication challenges in multi-agent systems.

Findings

Enhanced convergence and consensus in distributed inference

Scalable algorithms for complex graphical models

Effective communication protocols for distributed decision-making

Abstract

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.