Approximate inference techniques with expectation constraints
Tom Heskes, Manfred Opper, Wim Wiegerinck, Ole Winther, Onno Zoeter · Journal of Statistical Mechanics Theory and Experiment · 2005
This paper discusses inference problems in probabilistic graphical models that often occur in a machine learning setting. In particular it presents a unified view of several recently proposed approximation schemes. Expectation consistent approximations and expectation propagation are both shown to be related to Bethe free energies with weak consistency constraints , i.e. free energies where local approximations are only required to agree on certain statistics instead of full marginals.