Dynamic bayesian network modeling, learning, and inference: a survey (2021)
- Authors:
- Autor USP: LOPES, ALNEU DE ANDRADE - ICMC
- Unidade: ICMC
- DOI: 10.1109/ACCESS.2021.3105520
- Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS; INFERÊNCIA BAYESIANA; INTELIGÊNCIA ARTIFICIAL; REVISÃO SISTEMÁTICA
- Keywords: Dynamic Bayesian Networks; dynamic probabilistic graphical models; literature review; systematic literature review
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway
- Date published: 2021
- Source:
- Título: IEEE Access
- ISSN: 2169-3536
- Volume/Número/Paginação/Ano: v. 9, p. 117639-117648, 2021
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SHIGUIHARA PEDRO, e LOPES, Alneu de Andrade e MAURICIO, David. Dynamic bayesian network modeling, learning, and inference: a survey. IEEE Access, v. 9, p. 117639-117648, 2021Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2021.3105520. Acesso em: 03 mar. 2026. -
APA
Shiguihara Pedro,, Lopes, A. de A., & Mauricio, D. (2021). Dynamic bayesian network modeling, learning, and inference: a survey. IEEE Access, 9, 117639-117648. doi:10.1109/ACCESS.2021.3105520 -
NLM
Shiguihara Pedro, Lopes A de A, Mauricio D. Dynamic bayesian network modeling, learning, and inference: a survey [Internet]. IEEE Access. 2021 ; 9 117639-117648.[citado 2026 mar. 03 ] Available from: https://doi.org/10.1109/ACCESS.2021.3105520 -
Vancouver
Shiguihara Pedro, Lopes A de A, Mauricio D. Dynamic bayesian network modeling, learning, and inference: a survey [Internet]. IEEE Access. 2021 ; 9 117639-117648.[citado 2026 mar. 03 ] Available from: https://doi.org/10.1109/ACCESS.2021.3105520 - A multi-view approach for semi-supervised scientific paper classification
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Informações sobre o DOI: 10.1109/ACCESS.2021.3105520 (Fonte: oaDOI API)
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