Expectation Propagation
This provides a theoretical synthesis for researchers in approximate inference, but appears incremental as it builds on existing concepts.
The paper presents expectation propagation as a unifying framework for variational inference algorithms, connecting it with mean-field methods and belief propagation while exploring corrections and advanced applications.
Variational inference is a powerful concept that underlies many iterative approximation algorithms; expectation propagation, mean-field methods and belief propagations were all central themes at the school that can be perceived from this unifying framework. The lectures of Manfred Opper introduce the archetypal example of Expectation Propagation, before establishing the connection with the other approximation methods. Corrections by expansion about the expectation propagation are then explained. Finally some advanced inference topics and applications are explored in the final sections.