Expectation propagation (EP) is a technique in Bayesian machine learning.
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.
- Thomas Minka (August 2–5, 2001). "Expectation Propagation for Approximate Bayesian Inference". In Jack S. Breese, Daphne Koller. UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence. University of Washington, Seattle, Washington, USA. pp. 362–369.
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