Expectation propagation (EP) is a technique in Bayesian machine learning.
EP finds approximations to a probability distribution. It uses an iterative approach that uses 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.
If is a Gaussian , then is minimized with and being equal to the mean of and the covariance of , respectively; this is called moment matching.
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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This course aims at giving a broad overview of Bayesian inference, highlighting how the basic Bayesian paradigm proceeds, and the various methods that can be used to deal with the computational issues