The effect of combination functions on the complexity of relational Bayesian networks (2016)
- Authors:
- USP affiliated authors: COZMAN, FABIO GAGLIARDI - EP ; MAUÁ, DENIS DERATANI - IME
- Unidades: EP; IME
- Subjects: INFERÊNCIA BAYESIANA; INTELIGÊNCIA ARTIFICIAL; RACIOCÍNIO PROBABILÍSTICO
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Machine Learning Research
- ISSN: 1533-7928
- Volume/Número/Paginação/Ano: n. 52, p. 333-344, 2016
- Conference titles: International Conference on Probabilistic Graphical Models - PMLR
-
ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. The effect of combination functions on the complexity of relational Bayesian networks. Journal of Machine Learning Research. Brookline: Escola Politécnica, Universidade de São Paulo. Disponível em: http://proceedings.mlr.press/v52/maua16.pdf. Acesso em: 24 fev. 2026. , 2016 -
APA
Mauá, D. D., & Cozman, F. G. (2016). The effect of combination functions on the complexity of relational Bayesian networks. Journal of Machine Learning Research. Brookline: Escola Politécnica, Universidade de São Paulo. Recuperado de http://proceedings.mlr.press/v52/maua16.pdf -
NLM
Mauá DD, Cozman FG. The effect of combination functions on the complexity of relational Bayesian networks [Internet]. Journal of Machine Learning Research. 2016 ;( 52): 333-344.[citado 2026 fev. 24 ] Available from: http://proceedings.mlr.press/v52/maua16.pdf -
Vancouver
Mauá DD, Cozman FG. The effect of combination functions on the complexity of relational Bayesian networks [Internet]. Journal of Machine Learning Research. 2016 ;( 52): 333-344.[citado 2026 fev. 24 ] Available from: http://proceedings.mlr.press/v52/maua16.pdf - Dealing with cycles in graph-based probabilistic models: the case of Logical Credal Networks
- Probabilistic logic programming under the L-Stable semantics
- Specifying credal sets with probabilistic answer set programming
- The complexity of inferences and explanations in probabilistic logic programming
- The descriptive complexity of bayesian network specifications
- Robustifying sum-product networks
- The complexity of Bayesian networks specified by propositional and relational languages
- The finite model theory of bayesian networks: descriptive complexity
- Bayesian networks specified using propositional and relational constructs: combined, data, and domain complexity
- Fast local search methods for solving limited memory influence diagrams
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