PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Expectation Consistent Free Energies for Approximate Inference
Manfred Opper and Ole Winther
In: NIPS 2004, 14-16 Dec 2004, Vancouver.

Abstract

We propose a novel a framework for deriving approximations for intractable probabilistic models. This framework is based on a free energy (negative log marginal likelihood) and can be seen as a generalization of adaptive TAP and expectation propagation (EP) . The free energy is constructed from two approximating distributions which encode different aspects of the intractable model such a single node constraints and couplings and are by construction consistent on a chosen set of moments. We test the framework on a difficult benchmark problem with binary variables on fully connected graphs and 2D grid graphs. We find good performance using sets of moments which either specify factorized nodes or a spanning tree on the nodes (structured approximation). Surprisingly, the Bethe approximation gives very inferior results even on grids.

EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:810
Deposited By:Manfred Opper
Deposited On:01 January 2005