Expectation propagation (EP) is a technique in Bayesian machine learning.
[1] EP finds approximations to a probability distribution.
[1] It uses an iterative approach that uses the factorization structure of the target distribution.
[1] It differs from other Bayesian approximation approaches such as variational Bayesian methods.
[1] 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
[1] Variational Bayesian methods minimize
is minimized with
, respectively; this is called moment matching.
[1] Expectation propagation via moment matching plays a vital role in approximation for indicator functions that appear when deriving the message passing equations for TrueSkill.
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