Gauged mini-bucket elimination for approximate inference

Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations


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 lower bounds on Z, and is easier to optimize than the prior gauge-variational algorithm. We show that WMBE-G strictly improves the earlier WMBE approximation for symmetric models including Ising models with no magnetic field. Our experimental results demonstrate the effectiveness of WMBE-G even for generic, non-symmetric models.

Original languageEnglish (US)
Number of pages10
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: Apr 9 2018Apr 11 2018


Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
CityPlaya Blanca, Lanzarote, Canary Islands

ASJC Scopus subject areas

  • Statistics and Probability
  • Artificial Intelligence


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