Approximating the Bethe Partition Function

Abstract

When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy F, and is often strikingly accurate. However, it may converge only to a local optimum or may not converge at all. An algorithm was recently introduced by Weller and Jebara for attractive binary pairwise MRFs which is guaranteed to return an e-approximation to the global minimum of F in polynomial time provided the maximum degree Δ = O(log n), where n is the number of variables. Here we extend their approach and derive a new method based on analyzing first derivatives of F, which leads to much better performance and, for attractive models, yields a fully polynomial-time approximation scheme (FPTAS) without any degree restriction. Further, our methods apply to general (non-attractive) models, though with no polynomial time guarantee in this case, demonstrating that approximating log of the Bethe partition function, log ZB = - min F, for a general model to additive e-accuracy may be reduced to a discrete MAP inference problem. This allows the merits of the global Bethe optimum to be tested.