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.
More specifically, suppose we wish to approximate an intractable probability distribution with a tractable distribution . Expectation propagation achieves this approximation by minimizing the Kullback-Leibler divergence . Variational Bayesian methods minimize instead.
- Thomas Minka (August 2–5, 2001). "Expectation Propagation for Approximate Bayesian Inference". In Jack S. Breese, Daphne Koller (ed.). UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (PDF). University of Washington, Seattle, Washington, USA. pp. 362–369.
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