A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
This is an incremental improvement for researchers in machine learning and statistical inference, offering a more accurate and stable expansion method.
The paper tackles the problem of improving approximations in probabilistic graphical models by introducing a cluster-cumulant expansion based on belief propagation fixed points, which empirically shows better accuracy than the loop-series method.
We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) recently introduced in [1]. However, in contrast to the latter, the CCE enjoys the following important qualities: 1) it is defined for arbitrary state spaces 2) it is easily extended to fixed points of generalized belief propagation (GBP), 3) disconnected groups of variables will not contribute to the CCE and 4) the accuracy of the expansion empirically improves upon that of the LS. The CCE is based on the same Möbius transform as the Kikuchi approximation, but unlike GBP does not require storing the beliefs of the GBP-clusters nor does it suffer from convergence issues during belief updating.