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Gauged Mini-Bucket Elimination for Approximate Inference
[article]
2018
arXiv
pre-print
Computing the partition function Z of a discrete graphical model is a fundamental inference challenge. Since this is computationally intractable, variational approximations are often used in practice. Recently, so-called gauge transformations were used to improve variational lower bounds on Z. In this paper, we propose a new gauge-variational approach, termed WMBE-G, which combines gauge transformations with the weighted mini-bucket elimination (WMBE) method. WMBE-G can provide both upper and
arXiv:1801.01649v2
fatcat:yfblfpxqobbbziehfr7jwycsqu