Probabilistic graphical models specified by probabilistic logic programs: Semantics and complexity

Fabio Gagliardi Cozman, Denis Deratani Mau

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

We look at probabilistic logic programs as a specification language for probabilistic models, and study their interpretation and complexity. Acyclic programs specify Bayesian networks, and, depending on constraints on logical atoms, their inferential complexity reaches complexity classes #P, #NP, and even #EXP. We also investigate (cyclic) stratified probabilistic logic programs, showing that they have the same complexity as acyclic probabilistic logic programs, and that they can be depicted using chain graphs.

Original languageEnglish
Pages (from-to)110-122
Number of pages13
JournalJournal of Machine Learning Research
Volume52
Issue number2016
StatePublished - 2016
Externally publishedYes
Event8th International Conference on Probabilistic Graphical Models, PGM 2016 - Lugano, Switzerland
Duration: 6 Sep 20169 Sep 2016

Keywords

  • Complexity theory
  • Probabilistic logic programming

Fingerprint

Dive into the research topics of 'Probabilistic graphical models specified by probabilistic logic programs: Semantics and complexity'. Together they form a unique fingerprint.

Cite this