Expectation propagation (EP) is a technique in Bayesian machine learning, developed by Thomas Minka.
EP finds approximations to a probability distribution. It uses an iterative approach that leverages the factorization structure of the target distribution. It differs from other Bayesian approximation approaches such as Variational Bayesian methods.
Minka, T. (2001) "Expectation Propagation for Approximate Bayesian Inference"(Corrected pdf) In: Jack S. Breese, Daphne Koller (Eds.): UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001. (pages 362–369)