Advances in Learning Bayesian Networks of Bounded Treewidth

Autor(es)

Nie, Siqi ; Maua, Denis D. ; de Campos, Cassio P. ; Ji, Qiang ; Ghahramani, Z ; Welling, M ; Cortes, C ; Lawrence, ND ; Weinberger, KQ

• Tipo de documento: Artigo Científico
• Fonte: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017); v. 27, p. 9-pg., 2014-01-01.

Resumo

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)

• Processo FAPESP: 13/23197-4 - Algoritmos Eficientes para Tomada de Decisão sob Incerteza Baseada em Grafos
• Beneficiário: Denis Deratani Mauá
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado