Bucket renormalization for approximate inference

Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin

Research output: Contribution to journalArticlepeer-review


Probabilistic graphical models are a key tool in machine learning applications. Computing the partition function, i.e. normalizing constant, is a fundamental task of statistical inference but it is generally computationally intractable, leading to extensive study of approximation methods. Iterative variational methods are a popular and successful family of approaches. However, even state of the art variational methods can return poor results or fail to converge on difficult instances. In this paper, we instead consider computing the partition function via sequential summation over variables. We develop robust approximate algorithms by combining ideas from mini-bucket elimination with tensor network and renormalization group methods from statistical physics. The resulting 'convergence-free' methods show good empirical performance on both synthetic and real-world benchmark models, even for difficult instances.

Original languageEnglish (US)
Article number124022
JournalJournal of Statistical Mechanics: Theory and Experiment
Issue number12
StatePublished - Dec 20 2019


  • machine learning

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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