Expectation propagation

Expectation propagation (EP) is a technique in Bayesian machine learning, developed by Thomas Minka.

Expectation Propagation To approximate the integral of a function, approximate each factor by sequential moment-matching. For dynamic systems, it generalizes Iterative Extended Kalman filtering. For Markov nets, it generalizes belief propagation.