Belief Propagation

Belief propagation (BP) is a probabilistic inference algorithm used to approximate marginal probabilities in graphical models, aiming to efficiently solve complex problems where exact solutions are intractable. Current research focuses on extending BP's capabilities through integrations with neural networks (like Graph Neural Networks and Transformers), developing variations such as Gaussian Belief Propagation and Circular Belief Propagation to handle different data types and graph structures, and applying it to diverse fields including signal processing, robotics, and machine learning. These advancements are improving the accuracy and efficiency of BP for applications ranging from error-correcting code decoding and simultaneous localization and mapping to multi-object tracking and solving constraint optimization problems.