Inference and Sampling of K33-free Ising Models

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

We call an Ising model tractable when it is possible to compute its partition function value (statistical inference) in polynomial time. The tractability also implies an ability to sample configurations of this model in polynomial time. The notion of tractability extends the basic case of planar zero-field Ising models. Our starting point is to describe algorithms for the basic case, computing partition function and sampling efficiently. Then, we extend our tractable inference and sampling algorithms to models whose triconnected components are either planar or graphs of O(1) size. In particular, it results in a polynomial-time inference and sampling algorithms for K33 (minor)free topologies of zero-field Ising models—a generalization of planar graphs with a potentially unbounded genus.

Original languageEnglish (US)
Pages (from-to)3963-3972
Number of pages10
JournalProceedings of Machine Learning Research
Volume97
StatePublished - 2019
Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
Duration: Jun 9 2019Jun 15 2019

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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