| Full text | |
| Author(s): |
Nie, Siqi
;
Maua, Denis D.
;
de Campos, Cassio P.
;
Ji, Qiang
;
Ghahramani, Z
;
Welling, M
;
Cortes, C
;
Lawrence, ND
;
Weinberger, KQ
Total Authors: 9
|
| Document type: | Journal article |
| Source: | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017); v. 27, p. 9-pg., 2014-01-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 13/23197-4 - Efficient algorithms for graph-based decision making under uncertainty |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |