Advances in Learning Bayesian Networks of Bounded Treewidth Denis D. Mauá Rensselaer Polytechnic Institute University of São Paulo Troy, NY, USA
–Neural Information Processing Systems
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.
Neural Information Processing Systems
Mar-13-2024, 07:45:44 GMT
- Country:
- North America > United States
- New York > Rensselaer County > Troy (0.40)
- South America > Brazil
- São Paulo (0.40)
- North America > United States
- Genre:
- Research Report > Promising Solution (0.48)