Expectation Consistent Approximate Inference
We propose a novel framework for approximations to intractable probabilistic models which is based on a free energy formulation. The approximation can be understood as replacing an average over the original intractable distribution with a tractable one. It requires two tractable probability distributions which are made consistent on a set of moments and encode different features of the original intractable distribution. In this way we are able to use Gaussian approximations for models with discrete or bounded variables which allow us to include non-trivial correlations. These are neglected in many other methods. We test the framework on toy benchmark problems for binary variables on fully connected graphs and 2D grids and compare with other methods, such as loopy belief propagation. Good performance is already achieved by using single nodes as tractable substructures. Significant improvements are obtained when a spanning tree is used instead.