
A Generalized Loop Correction Method for Approximate Inference in Graphical Models
Belief Propagation (BP) is one of the most popular methods for inference...
read it

MCMC assisted by Belief Propagaion
Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most...
read it

Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
We suggest a new methodology for analysis and approximate computations o...
read it

Gauging Variational Inference
Computing partition function is the most important statistical inference...
read it

Loop Calculus and BootstrapBelief Propagation for Perfect Matchings on Arbitrary Graphs
This manuscript discusses computation of the Partition Function (PF) and...
read it

Dual Decomposition from the Perspective of Relax, Compensate and then Recover
Relax, Compensate and then Recover (RCR) is a paradigm for approximate i...
read it

Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be gr...
read it
Approximate inference on planar graphs using Loop Calculus and Belief Propagation
We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006b) allows to express the exact partition function Z of a graphical model as a finite sum of terms that can be evaluated once the belief propagation (BP) solution is known. In general, full summation over all correction terms is intractable. We develop an algorithm for the approach presented in Chertkov et al. (2008) which represents an efficient truncation scheme on planar graphs and a new representation of the series in terms of Pfaffians of matrices. We analyze in detail both the loop series and the Pfaffian series for models with binary variables and pairwise interactions, and show that the first term of the Pfaffian series can provide very accurate approximations. The algorithm outperforms previous truncation schemes of the loop series and is competitive with other stateoftheart methods for approximate inference.
READ FULL TEXT
Comments
There are no comments yet.