Gauged mini-bucket elimination for approximate inference
- Title
- Gauged mini-bucket elimination for approximate inference
- Authors
- AHN, SUNGSOO; Chertkov, Michael; Shin, Jinwoo; Weller, Adrian
- Date Issued
- 2018-04
- Publisher
- PMLR
- Abstract
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/109517
- Article Type
- Conference
- Citation
- 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018, page. 10 - 19, 2018-04
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