Circular Belief Propagation for Approximate Probabilistic Inference

arXiv:2403.12106v1 Announce Type: cross
Abstract: Belief Propagation (BP) is a simple probabilistic inference algorithm, consisting of passing messages between nodes of a graph representing a probability distribution. Its analogy with a neural network suggests that it could have far-ranging applications for neuroscience and artificial intelligence. Unfortunately, it is only exact when applied to cycle-free graphs, which restricts the potential of the algorithm. In this paper, we propose Circular Belief Propagation (CBP), an extension of BP which limits the detrimental effects …

abstract algorithm analogy applications artificial artificial intelligence arxiv belief cs.ai cs.lg distribution graph inference intelligence messages network neural network neuroscience nodes probability propagation simple type