GOBNILP: Learning Bayesian network structure with integer programming

James Cussens
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:605-608, 2020.

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.

Cite this Paper


BibTeX
@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 = {Manfred Jaeger and Thomas Dyhre Nielsen}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/cussens20a/cussens20a.pdf}, url = { http://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. } }
Endnote
%0 Conference Paper %T GOBNILP: Learning Bayesian network structure with integer programming %A James Cussens %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-cussens20a %I PMLR %P 605--608 %U http://proceedings.mlr.press/v138/cussens20a.html %V 138 %X 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.
APA
Cussens, J.. (2020). GOBNILP: Learning Bayesian network structure with integer programming. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:605-608 Available from http://proceedings.mlr.press/v138/cussens20a.html .

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