Approximate Inference in Probabilistic Models Lecture within the tutorial Statistical Methods in Learning Speaker: Manfred Opper (Southampton University) I present a framework for approximate inference in probabilistic models which generalizes the TAP (Thouless, Anderson & Palmer) approach to the statistical mechanics of spin-glass models. Using an approximate expansion of a suitably defined Gibbs free energy around a tractable model, we can compute certain posterior expectations for Bayesian models with good accuracy. We give applications and show that the method can also be used for a faster computation of resampling averages. This is joint work with Ole Winther ©Copyright 2004 Authors