Credal sum-product networks (2017)
- Autores:
- Autores USP: MAUÁ, DENIS DERATANI - IME ; COZMAN, FABIO GAGLIARDI - EP
- Unidades: IME; EP
- Assuntos: INTELIGÊNCIA ARTIFICIAL; MODELOS PARA PROCESSOS ESTOCÁSTICOS
- Idioma: Inglês
- Imprenta:
- Fonte:
- Título do periódico: PMLR: Proceedings of Machine Learning Research
- ISSN: 1938-7228
- Volume/Número/Paginação/Ano: n. 62, p. 205-216, 2017
- Nome do evento: International Symposium on Imprecise Probability: Theories and Applications - ISIPTA
-
ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi e CONATY, Diarmaid. Credal sum-product networks. PMLR: Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: http://proceedings.mlr.press/v62/mau*C3*A117a.html. Acesso em: 18 set. 2024. , 2017 -
APA
Mauá, D. D., Cozman, F. G., & Conaty, D. (2017). Credal sum-product networks. PMLR: Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de http://proceedings.mlr.press/v62/mau*C3*A117a.html -
NLM
Mauá DD, Cozman FG, Conaty D. Credal sum-product networks [Internet]. PMLR: Proceedings of Machine Learning Research. 2017 ;( 62): 205-216.[citado 2024 set. 18 ] Available from: http://proceedings.mlr.press/v62/mau*C3*A117a.html -
Vancouver
Mauá DD, Cozman FG, Conaty D. Credal sum-product networks [Internet]. PMLR: Proceedings of Machine Learning Research. 2017 ;( 62): 205-216.[citado 2024 set. 18 ] Available from: http://proceedings.mlr.press/v62/mau*C3*A117a.html - Specifying credal sets with probabilistic answer set programming
- On the complexity of propositional and relational credal networks
- DL-Lite Bayesian networks: a tractable probabilistic graphical model
- On the semantics and complexity of probabilistic logic programs
- The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference
- Fast local search methods for solving limited memory influence diagrams
- The complexity of inferences and explanations in probabilistic logic programming
- The finite model theory of bayesian networks: descriptive complexity
- A tractable class of model counting problems
- The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws
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