Reliable Belief Propagation: Recent Theoretical and Practical Advances

Belief propagation (BP) is an effective approximate inference method but lacks theoretical guarantees for loopy graphs. We discuss the optimization landscape and the message dynamics and how this helps to understand the behavior of message passing algorithms. These insights suggest several improvements. Specifically, we consider iterative initialization strategies, optimized message scheduling methods, and structural modifications, to improve the convergence behavior and accuracy while maintaining the model's interpretability. We then evaluate the different modifications on signal detection problems in MIMO systems, which is a particularly challenging application for message passing algorithms. Our experimental results show consistent improvements over standard BP with minimal increase in computational burden.