@misc{https://doi.org/10.48550/arxiv.1202.3713,

  url = {https://arxiv.org/abs/1202.3713},
  
  author = {Cussens, James},
  
  keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Bayesian network learning with cutting planes},
  
  publisher = {arXiv},
  
  year = {2012},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{BARTLETT2017258,
title = {Integer Linear Programming for the Bayesian network structure learning problem},
journal = {Artificial Intelligence},
volume = {244},
pages = {258-271},
year = {2017},
note = {Combining Constraint Solving with Mining and Learning},
issn = {0004-3702},
url = {https://www.sciencedirect.com/science/article/pii/S0004370215000417},
author = {Mark Bartlett and James Cussens},
keywords = {Bayesian networks, Integer Linear Programming, Constrained optimisation, Cutting planes, Separation}
}

@article{cussens2017bayesian,
  title={Bayesian network structure learning with integer programming: Polytopes, facets and complexity},
  author={Cussens, James and J{\"a}rvisalo, Matti and Korhonen, Janne H and Bartlett, Mark},
  journal={Journal of Artificial Intelligence Research},
  volume={58},
  pages={185--229},
  year={2017},
  url={https://www.jair.org/index.php/jair/article/view/11041}
}


@InProceedings{pmlr-v138-cussens20a,
  title = 	 {GOBNILP: Learning Bayesian network structure with integer programming},
  author =       {Cussens, James},
  booktitle = 	 {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
  pages = 	 {605--608},
  year = 	 {2020},
  editor = 	 {Jaeger, Manfred and Nielsen, Thomas Dyhre},
  volume = 	 {138},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--25 Sep},
  publisher =    {PMLR},
  url = 	 {https://proceedings.mlr.press/v138/cussens20a.html},
  abstract = 	 {The GOBNILP system for learning Bayesian networks is
 presented. Both the Python and C implementations are discussed. The
 usefulness of learning multiple BNs is highlighted. Current work on
 ‘pricing in’ new integer programming variables is presented.
 }
}
