Integer linear programming for the Bayesian network structure learning problem

DOI10.1016/j.artint.2015.03.003zbMath1404.68094OpenAlexW1981696983MaRDI QIDQ511785

Mark Bartlett, James Cussens

Publication date: 22 February 2017

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://rgu-repository.worktribe.com/output/816263

zbMATH Keywords

Bayesian networksinteger linear programmingcutting planesseparationconstrained optimisation

Mathematics Subject Classification ID

Integer programming (90C10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (12)

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation ⋮ Entropy-based pruning for learning Bayesian networks using BIC ⋮ Hierarchical estimation of parameters in Bayesian networks ⋮ Causal structure learning: a combinatorial perspective ⋮ Revising the structure of Bayesian network classifiers in the presence of missing data ⋮ Learning the structure of Bayesian networks with ancestral and/or heuristic partition ⋮ Studying the effective brain connectivity using multiregression dynamic models ⋮ Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks ⋮ Optimization problems for machine learning: a survey ⋮ Approximate structure learning for large Bayesian networks ⋮ Learning Bayesian networks with local structure, mixed variables, and exact algorithms ⋮ Discovering causal graphs with cycles and latent confounders: an exact branch-and-bound approach

Uses Software

• SCIP
• Concorde

Cites Work

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