Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams (2016)
- Autor:
- Autor USP: MAUÁ, DENIS DERATANI - IME
- Unidade: IME
- DOI: 10.1016/j.ijar.2015.03.007
- Subjects: INTELIGÊNCIA ARTIFICIAL; PROBABILIDADE APLICADA; RACIOCÍNIO PROBABILÍSTICO
- Language: Inglês
- Imprenta:
- Source:
- Título: International Journal of Approximate Reasoning
- ISSN: 1873-4731
- Volume/Número/Paginação/Ano: v. 68, p. 211-229, Jan. 2016
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MAUÁ, Denis Deratani. Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams. International Journal of Approximate Reasoning, v. 68, n. Ja 2016, p. 211-229, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2015.03.007. Acesso em: 27 jan. 2026. -
APA
Mauá, D. D. (2016). Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams. International Journal of Approximate Reasoning, 68( Ja 2016), 211-229. doi:10.1016/j.ijar.2015.03.007 -
NLM
Mauá DD. Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams [Internet]. International Journal of Approximate Reasoning. 2016 ; 68( Ja 2016): 211-229.[citado 2026 jan. 27 ] Available from: https://doi.org/10.1016/j.ijar.2015.03.007 -
Vancouver
Mauá DD. Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams [Internet]. International Journal of Approximate Reasoning. 2016 ; 68( Ja 2016): 211-229.[citado 2026 jan. 27 ] Available from: https://doi.org/10.1016/j.ijar.2015.03.007 - Modelos de tópicos na classificação automática de resenhas de usuário
- Hidden Markov models with set-valued parameters
- Initialization heuristics for greedy bayesian network structure learning
- Advances in learning Bayesian networks of bounded treewidth
- Time robust trees: using temporal invariance to improve generalization
- Better initialization heuristics for order-based bayesian network structure learning
- International Journal of Approximate Reasoning
- Early classification of time series by Hidden Markov Models with set-valued parameters
- Approximation complexity of maximum a posteriori inference in sum-product networks
- Advances in automatically solving the ENEM
Informações sobre o DOI: 10.1016/j.ijar.2015.03.007 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
