Abstract
Motivated by distributed inference applications in unreliable communication networks, we adapt the popular (sum-product) belief propagation (BP) algorithm under the constraint of discrete-valued messages. We show that, in contrast to conventional BP, the optimal message-generation rules are node-dependent and iteration-dependent, each rule making explicit use of local memory from all past iterations. These results expose both the intractability of optimal design and an inherent structure that can be exploited for tractable approximate design. We propose one such approximation and demonstrate its efficacy on canonical examples. We also discuss extensions to communication networks with lossy links (e.g., erasures) or topologies that differ from the graph underlying the probabilistic model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
