## 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 K_{33} (minor)-free topologies of zero-field Ising models - a generalization of planar graphs with a potentially unbounded genus.

Original language | English |
---|---|

Title of host publication | 36th International Conference on Machine Learning, ICML 2019 |

Publisher | International Machine Learning Society (IMLS) |

Pages | 6996-7005 |

Number of pages | 10 |

ISBN (Electronic) | 9781510886988 |

Publication status | Published - 2019 |

Event | 36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States Duration: 9 Jun 2019 → 15 Jun 2019 |

### Publication series

Name | 36th International Conference on Machine Learning, ICML 2019 |
---|---|

Volume | 2019-June |

### Conference

Conference | 36th International Conference on Machine Learning, ICML 2019 |
---|---|

Country/Territory | United States |

City | Long Beach |

Period | 9/06/19 → 15/06/19 |

## Fingerprint

Dive into the research topics of 'Inference and sampling of K_{33}-free ising models'. Together they form a unique fingerprint.