# Gauged Mini-Bucket Elimination for Approximate Inference

arXiv: https://arxiv.org/abs/1801.01649 · HTML: https://mlsanity.com/paper/1801.01649/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Understanding the Behavior of Belief Propagation](https://mlsanity.com/paper/2209.05464/) (2022)
- [On Probabilistic Inference Through Parametric Tensor Decomposition in Base Tensor Networks](https://mlsanity.com/paper/2609.23774/) (2026)

## Abstract (arXiv)

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, nonsymmetric models.