  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.
