A
pdf version of a preprint (315kB) is available.
The contribution builds on the following papers:
- M. Bartlett, J. Cussens:
Advances in Bayesian network learning using integer programming.
In Uncertainty in Artificial Intelligence. Proceedings of the
29th Conference (A. Nicholson, P. Smyth eds.),
AUAI Press, Corvallis 2013, pp. 182-191.
- R. Hemmecke, S. Lindner, M. Studeny:
Characteristic imsets for learning Bayesian network structure.
International Journal of Approximate Reasoning
53 (2012), n. 9, pp. 1336-1349.
- S. L. Lauritzen (1996).
Graphical Models.
Clarendon Press, Oxford.
- S. Lindner (2012)
Discrete optimization in machine learning: learning Bayesian network structures and conditional independence implication.
PhD thesis, TU Munich.
- M. Studeny, D. Haws:
Learning Bayesian network structure: towards the essential graph by integer linear programming tools.
International Journal of Approximate Reasoning
55 (2014), pp. 1043-1071.
Moreover, the paper is an extended version of the conference proceedings paper: