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

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