GOBNILP

GOBNILP (Globally Optimal Bayesian Network learning using Integer Linear Programming) is a C program which learns Bayesian networks from complete discrete data or from local scores. GOBNILP uses the SCIP framework for Constraint Integer Programming.

Keywords for this software

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References in zbMATH (referenced in 5 articles )

Showing results 1 to 5 of 5.
Sorted by year (citations)

  1. Cussens, James; Haws, David; Studený, Milan: Polyhedral aspects of score equivalence in Bayesian network structure learning (2017)
  2. Djordjilović, Vera; Chiogna, Monica; Vomlel, Jiří: An empirical comparison of popular structure learning algorithms with a view to gene network inference (2017)
  3. Haws, David; Cussens, James; Studený, Milan: Polyhedral approaches to learning Bayesian networks (2017)
  4. Suzuki, Joe: An efficient Bayesian network structure learning strategy (2017)
  5. Studený, Milan; Haws, David: Learning Bayesian network structure: towards the essential graph by integer linear programming tools (2014)


Further publications can be found at: https://www.cs.york.ac.uk/aig/sw/gobnilp/#papers