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Expectation Propagation

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Advanced Machine Learning Tutorial Lecture

Expectation propagation is an algorithm for Bayesian machine learning that is especially well-suited to large databases and dynamic systems. Given prior knowledge expressed as a graphical model, it tunes the parameters of a “simple” probability distribution (such as a Gaussian) to best match the posterior distribution (which, in its exact form, could be very complex). This simplified posterior can be used to describe the data, make predictions, and quickly incorporate new data. Expectation propagation has been successfully applied to visual tracking, wireless communication, document analysis, diagram analysis, and matchmaking in online games.

This talk is part of the Machine Learning @ CUED series.

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