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Artigo de periodico
Bayesian networks supporting management practices: a multifaceted perspective based on the literature (2024)
Juliani, Fernando; Maciel, Carlos Dias
Source: International Journal of Information Management Data Insights. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, BIBLIOMETRIA, TOMADA DE DECISÃO, ENGENHARIA ELÉTRICA
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ABNT
JULIANI, Fernando e MACIEL, Carlos Dias. Bayesian networks supporting management practices: a multifaceted perspective based on the literature. International Journal of Information Management Data Insights, v. 4, n. 1, p. 1-17, 2024Tradução . . Disponível em: http://dx.doi.org/10.1016/j.jjimei.2024.100231. Acesso em: 12 nov. 2024.
APA
Juliani, F., & Maciel, C. D. (2024). Bayesian networks supporting management practices: a multifaceted perspective based on the literature. International Journal of Information Management Data Insights, 4( 1), 1-17. doi:10.1016/j.jjimei.2024.100231
NLM
Juliani F, Maciel CD. Bayesian networks supporting management practices: a multifaceted perspective based on the literature [Internet]. International Journal of Information Management Data Insights. 2024 ; 4( 1): 1-17.[citado 2024 nov. 12 ] Available from: http://dx.doi.org/10.1016/j.jjimei.2024.100231
Vancouver
Juliani F, Maciel CD. Bayesian networks supporting management practices: a multifaceted perspective based on the literature [Internet]. International Journal of Information Management Data Insights. 2024 ; 4( 1): 1-17.[citado 2024 nov. 12 ] Available from: http://dx.doi.org/10.1016/j.jjimei.2024.100231
Trabalho de evento
Quantifying uncertainty in bayesian networks structural learning (2024)
Barth, Vitor Bruno de Oliveira; Caetano, Henrique de Oliveira; Maciel, Carlos Dias ; Aiello, Marco
Source: Proceedings. Conference titles: IEEE International Conference on Evolving and Adaptive Intelligent Systems. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, APRENDIZADO COMPUTACIONAL, ENGENHARIA ELÉTRICA
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BARTH, Vitor Bruno de Oliveira et al. Quantifying uncertainty in bayesian networks structural learning. 2024, Anais.. Picataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2024. Disponível em: https://dx.doi.org/10.1109/EAIS58494.2024.10570017. Acesso em: 12 nov. 2024.
APA
Barth, V. B. de O., Caetano, H. de O., Maciel, C. D., & Aiello, M. (2024). Quantifying uncertainty in bayesian networks structural learning. In Proceedings. Picataway, NJ, USA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/EAIS58494.2024.10570017
NLM
Barth VB de O, Caetano H de O, Maciel CD, Aiello M. Quantifying uncertainty in bayesian networks structural learning [Internet]. Proceedings. 2024 ;[citado 2024 nov. 12 ] Available from: https://dx.doi.org/10.1109/EAIS58494.2024.10570017
Vancouver
Barth VB de O, Caetano H de O, Maciel CD, Aiello M. Quantifying uncertainty in bayesian networks structural learning [Internet]. Proceedings. 2024 ;[citado 2024 nov. 12 ] Available from: https://dx.doi.org/10.1109/EAIS58494.2024.10570017
Artigo de periodico
A bayesian hierarchical model to create synthetic power distribution systems (2024)
Caetano, Henrique de Oliveira; Desuó Neto, Luiz; Fogliatto, Matheus de Souza Sant'Anna ; Ribeiro, Vitor P; Balestieri, José Antonio Perrella; Maciel, Carlos Dias
Source: Electric Power Systems Research. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, INFERÊNCIA BAYESIANA, GEOREF, ENGENHARIA ELÉTRICA
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CAETANO, Henrique de Oliveira et al. A bayesian hierarchical model to create synthetic power distribution systems. Electric Power Systems Research, v. 235, p. 1-8, 2024Tradução . . Disponível em: https://dx.doi.org/10.1016/j.epsr.2024.110706. Acesso em: 12 nov. 2024.
APA
Caetano, H. de O., Desuó Neto, L., Fogliatto, M. de S. S. 'A., Ribeiro, V. P., Balestieri, J. A. P., & Maciel, C. D. (2024). A bayesian hierarchical model to create synthetic power distribution systems. Electric Power Systems Research, 235, 1-8. doi:10.1016/j.epsr.2024.110706
NLM
Caetano H de O, Desuó Neto L, Fogliatto M de SS'A, Ribeiro VP, Balestieri JAP, Maciel CD. A bayesian hierarchical model to create synthetic power distribution systems [Internet]. Electric Power Systems Research. 2024 ; 235 1-8.[citado 2024 nov. 12 ] Available from: https://dx.doi.org/10.1016/j.epsr.2024.110706
Vancouver
Caetano H de O, Desuó Neto L, Fogliatto M de SS'A, Ribeiro VP, Balestieri JAP, Maciel CD. A bayesian hierarchical model to create synthetic power distribution systems [Internet]. Electric Power Systems Research. 2024 ; 235 1-8.[citado 2024 nov. 12 ] Available from: https://dx.doi.org/10.1016/j.epsr.2024.110706
Artigo de periodico
Bayesian network structural learning using adaptive genetic algorithm with varying population size (2023)
Ribeiro, Rafael Rodrigues Mendes; Maciel, Carlos Dias
Source: Machine Learning and Knowledge Extraction. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, ALGORITMOS GENÉTICOS, ENGENHARIA ELÉTRICA
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ABNT
RIBEIRO, Rafael Rodrigues Mendes e MACIEL, Carlos Dias. Bayesian network structural learning using adaptive genetic algorithm with varying population size. Machine Learning and Knowledge Extraction, v. 5, p. 1877-1887, 2023Tradução . . Disponível em: https://dx.doi.org/10.3390/make5040090. Acesso em: 12 nov. 2024.
APA
Ribeiro, R. R. M., & Maciel, C. D. (2023). Bayesian network structural learning using adaptive genetic algorithm with varying population size. Machine Learning and Knowledge Extraction, 5, 1877-1887. doi:10.3390/make5040090
NLM
Ribeiro RRM, Maciel CD. Bayesian network structural learning using adaptive genetic algorithm with varying population size [Internet]. Machine Learning and Knowledge Extraction. 2023 ; 5 1877-1887.[citado 2024 nov. 12 ] Available from: https://dx.doi.org/10.3390/make5040090
Vancouver
Ribeiro RRM, Maciel CD. Bayesian network structural learning using adaptive genetic algorithm with varying population size [Internet]. Machine Learning and Knowledge Extraction. 2023 ; 5 1877-1887.[citado 2024 nov. 12 ] Available from: https://dx.doi.org/10.3390/make5040090
Trabalho de evento-resumo
Uma abordagem Bayesiana na tarefa de segmentação semântica (2022)
Consoni, João Lucas Foltran; Rosa, Nícolas dos Santos ; Terra, Marco Henrique
Source: Livro de Resumos Expandidos. Conference titles: Simpósio Internacional de Iniciação Científica e Tecnológica da Universidade de São Paulo - SIICUSP. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, REDES NEURAIS, ENGENHARIA ELÉTRICA
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CONSONI, João Lucas Foltran e ROSA, Nícolas dos Santos e TERRA, Marco Henrique. Uma abordagem Bayesiana na tarefa de segmentação semântica. 2022, Anais.. São Carlos, SP: EESC/USP, 2022. Disponível em: http://soac.eesc.usp.br/index.php/siicusp/30siicusp/paper/viewFile/3523/2352. Acesso em: 12 nov. 2024.
APA
Consoni, J. L. F., Rosa, N. dos S., & Terra, M. H. (2022). Uma abordagem Bayesiana na tarefa de segmentação semântica. In Livro de Resumos Expandidos. São Carlos, SP: EESC/USP. Recuperado de http://soac.eesc.usp.br/index.php/siicusp/30siicusp/paper/viewFile/3523/2352
NLM
Consoni JLF, Rosa N dos S, Terra MH. Uma abordagem Bayesiana na tarefa de segmentação semântica [Internet]. Livro de Resumos Expandidos. 2022 ;[citado 2024 nov. 12 ] Available from: http://soac.eesc.usp.br/index.php/siicusp/30siicusp/paper/viewFile/3523/2352
Vancouver
Consoni JLF, Rosa N dos S, Terra MH. Uma abordagem Bayesiana na tarefa de segmentação semântica [Internet]. Livro de Resumos Expandidos. 2022 ;[citado 2024 nov. 12 ] Available from: http://soac.eesc.usp.br/index.php/siicusp/30siicusp/paper/viewFile/3523/2352
Artigo de periodico
Data-driven network dynamical model of rat brains during acute ictogenesis (2022)
Tsukahara, Victor Hugo Batista; Oliveira Júnior, Jordão Natal de; Barth, Vitor Bruno de Oliveira; Oliveira, Jasiara Carla de; Cota, Vinicius Rosa ; Maciel, Carlos Dias
Source: Frontiers in Neural Circuits. Unidade: EESC
Subjects: EPILEPSIA, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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ABNT
TSUKAHARA, Victor Hugo Batista et al. Data-driven network dynamical model of rat brains during acute ictogenesis. Frontiers in Neural Circuits, v. 16, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.3389/fncir.2022.747910. Acesso em: 12 nov. 2024.
APA
Tsukahara, V. H. B., Oliveira Júnior, J. N. de, Barth, V. B. de O., Oliveira, J. C. de, Cota, V. R., & Maciel, C. D. (2022). Data-driven network dynamical model of rat brains during acute ictogenesis. Frontiers in Neural Circuits, 16, 1-13. doi:10.3389/fncir.2022.747910
NLM
Tsukahara VHB, Oliveira Júnior JN de, Barth VB de O, Oliveira JC de, Cota VR, Maciel CD. Data-driven network dynamical model of rat brains during acute ictogenesis [Internet]. Frontiers in Neural Circuits. 2022 ; 16 1-13.[citado 2024 nov. 12 ] Available from: https://doi.org/10.3389/fncir.2022.747910
Vancouver
Tsukahara VHB, Oliveira Júnior JN de, Barth VB de O, Oliveira JC de, Cota VR, Maciel CD. Data-driven network dynamical model of rat brains during acute ictogenesis [Internet]. Frontiers in Neural Circuits. 2022 ; 16 1-13.[citado 2024 nov. 12 ] Available from: https://doi.org/10.3389/fncir.2022.747910
Trabalho de evento
Sliding-priors for bayesian information fusion in SCADA+PMU-based state estimation (2022)
Camões, Francisco; Massignan, Julio Augusto Druzina; Miranda, Vladimiro; London Junior, João Bosco Augusto
Source: Proceedings. Conference titles: International Conference on Probabilistic Methods Applied to Power Systems. Unidade: EESC
Subjects: SISTEMAS ELÉTRICOS DE POTÊNCIA, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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ABNT
CAMÕES, Francisco et al. Sliding-priors for bayesian information fusion in SCADA+PMU-based state estimation. 2022, Anais.. Piscataway, NJ, USA: IEEE, 2022. Disponível em: https://doi.org/10.1109/PMAPS53380.2022.9810558. Acesso em: 12 nov. 2024.
APA
Camões, F., Massignan, J. A. D., Miranda, V., & London Junior, J. B. A. (2022). Sliding-priors for bayesian information fusion in SCADA+PMU-based state estimation. In Proceedings. Piscataway, NJ, USA: IEEE. doi:10.1109/PMAPS53380.2022.9810558
NLM
Camões F, Massignan JAD, Miranda V, London Junior JBA. Sliding-priors for bayesian information fusion in SCADA+PMU-based state estimation [Internet]. Proceedings. 2022 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/PMAPS53380.2022.9810558
Vancouver
Camões F, Massignan JAD, Miranda V, London Junior JBA. Sliding-priors for bayesian information fusion in SCADA+PMU-based state estimation [Internet]. Proceedings. 2022 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/PMAPS53380.2022.9810558
Artigo de periodico
Bayesian inference approach for information fusion in distribution system state estimation (2022)
Massignan, Julio Augusto Druzina; London Junior, João Bosco Augusto ; Bessani, Michel; Maciel, Carlos Dias ; Fannucchi, Rodrigo Z.; Miranda, Vladimiro
Source: IEEE Transactions on Smart Grid. Unidade: EESC
Subjects: DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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MASSIGNAN, Julio Augusto Druzina et al. Bayesian inference approach for information fusion in distribution system state estimation. IEEE Transactions on Smart Grid, v. 13, n. Ja 2022, p. 526-540, 2022Tradução . . Disponível em: https://doi.org/10.1109/TSG.2021.3128053. Acesso em: 12 nov. 2024.
APA
Massignan, J. A. D., London Junior, J. B. A., Bessani, M., Maciel, C. D., Fannucchi, R. Z., & Miranda, V. (2022). Bayesian inference approach for information fusion in distribution system state estimation. IEEE Transactions on Smart Grid, 13( Ja 2022), 526-540. doi:10.1109/TSG.2021.3128053
NLM
Massignan JAD, London Junior JBA, Bessani M, Maciel CD, Fannucchi RZ, Miranda V. Bayesian inference approach for information fusion in distribution system state estimation [Internet]. IEEE Transactions on Smart Grid. 2022 ; 13( Ja 2022): 526-540.[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/TSG.2021.3128053
Vancouver
Massignan JAD, London Junior JBA, Bessani M, Maciel CD, Fannucchi RZ, Miranda V. Bayesian inference approach for information fusion in distribution system state estimation [Internet]. IEEE Transactions on Smart Grid. 2022 ; 13( Ja 2022): 526-540.[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/TSG.2021.3128053
Trabalho de evento
Bayesian network for hydrological model: an inference approach (2022)
Ribeiro, Vitor P.; Cunha, Ângela Silviane Moura; Duarte, Sergio Nascimento ; Padovani, Carlos Roberto ; Marques, Patricia Algélica Alves ; Maciel, Carlos Dias ; Balestieri, José Antonio Perrella
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: ESALQ, EESC
Subjects: EVAPOTRANSPIRAÇÃO, BALANÇO HÍDRICO, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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RIBEIRO, Vitor P. et al. Bayesian network for hydrological model: an inference approach. 2022, Anais.. Piscataway, NJ, USA: IEEE, 2022. Disponível em: https://doi.org/10.1109/IJCNN55064.2022.9892468. Acesso em: 12 nov. 2024.
APA
Ribeiro, V. P., Cunha, Â. S. M., Duarte, S. N., Padovani, C. R., Marques, P. A. A., Maciel, C. D., & Balestieri, J. A. P. (2022). Bayesian network for hydrological model: an inference approach. In Proceedings. Piscataway, NJ, USA: IEEE. doi:10.1109/IJCNN55064.2022.9892468
NLM
Ribeiro VP, Cunha ÂSM, Duarte SN, Padovani CR, Marques PAA, Maciel CD, Balestieri JAP. Bayesian network for hydrological model: an inference approach [Internet]. Proceedings. 2022 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892468
Vancouver
Ribeiro VP, Cunha ÂSM, Duarte SN, Padovani CR, Marques PAA, Maciel CD, Balestieri JAP. Bayesian network for hydrological model: an inference approach [Internet]. Proceedings. 2022 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892468
Trabalho de evento
Bayesian optimization for efficient tuning of visual servo and computed torque controllers in a reinforcement learning scenario (2021)
Ribeiro, Eduardo Godinho; Mendes, Raul de Queiroz; Terra, Marco Henrique ; Grassi Júnior, Valdir
Source: Proceedings. Conference titles: International Conference on Advanced Robotics - ICAR. Unidade: EESC
Subjects: SISTEMAS DE CONTROLE, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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RIBEIRO, Eduardo Godinho et al. Bayesian optimization for efficient tuning of visual servo and computed torque controllers in a reinforcement learning scenario. 2021, Anais.. Los Alamitos, CA, USA: IEEE, 2021. Disponível em: https://doi.org/10.1109/ICAR53236.2021.9659363. Acesso em: 12 nov. 2024.
APA
Ribeiro, E. G., Mendes, R. de Q., Terra, M. H., & Grassi Júnior, V. (2021). Bayesian optimization for efficient tuning of visual servo and computed torque controllers in a reinforcement learning scenario. In Proceedings. Los Alamitos, CA, USA: IEEE. doi:10.1109/ICAR53236.2021.9659363
NLM
Ribeiro EG, Mendes R de Q, Terra MH, Grassi Júnior V. Bayesian optimization for efficient tuning of visual servo and computed torque controllers in a reinforcement learning scenario [Internet]. Proceedings. 2021 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/ICAR53236.2021.9659363
Vancouver
Ribeiro EG, Mendes R de Q, Terra MH, Grassi Júnior V. Bayesian optimization for efficient tuning of visual servo and computed torque controllers in a reinforcement learning scenario [Internet]. Proceedings. 2021 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/ICAR53236.2021.9659363
Artigo de periodico
Multiple households very short-term load forecasting using bayesian networks (2020)
Bessani, Michel; Massignan, Julio Augusto Druzina; Santos, Talysson Manoel de Oliveira; London Junior, João Bosco Augusto ; Maciel, Carlos Dias
Source: Electric Power Systems Research. Unidade: EESC
Subjects: POTENCIAL ELÉTRICO, ENERGIA ELÉTRICA, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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ABNT
BESSANI, Michel et al. Multiple households very short-term load forecasting using bayesian networks. Electric Power Systems Research, v. 189, p. 1-7, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.epsr.2020.106733. Acesso em: 12 nov. 2024.
APA
Bessani, M., Massignan, J. A. D., Santos, T. M. de O., London Junior, J. B. A., & Maciel, C. D. (2020). Multiple households very short-term load forecasting using bayesian networks. Electric Power Systems Research, 189, 1-7. doi:10.1016/j.epsr.2020.106733
NLM
Bessani M, Massignan JAD, Santos TM de O, London Junior JBA, Maciel CD. Multiple households very short-term load forecasting using bayesian networks [Internet]. Electric Power Systems Research. 2020 ; 189 1-7.[citado 2024 nov. 12 ] Available from: https://doi.org/10.1016/j.epsr.2020.106733
Vancouver
Bessani M, Massignan JAD, Santos TM de O, London Junior JBA, Maciel CD. Multiple households very short-term load forecasting using bayesian networks [Internet]. Electric Power Systems Research. 2020 ; 189 1-7.[citado 2024 nov. 12 ] Available from: https://doi.org/10.1016/j.epsr.2020.106733
Trabalho de evento
Bayesian reconstruction for digital breast tomosynthesis using a non-local Gaussian Markov random field a priori model (2019)
Salvadeo, Denis H. P.; Vimieiro, Rodrigo de Barros; Vieira, Marcelo Andrade da Costa; Maidment, Andrew D. A.
Source: Proceedings of SPIE. Conference titles: Medical Imaging 2019 : physics of medical imaging. Unidade: EESC
Subjects: MAMOGRAFIA, IMAGEM DIGITAL, INFERÊNCIA BAYESIANA, TOMOGRAFIA, ENGENHARIA ELÉTRICA
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ABNT
SALVADEO, Denis H. P. et al. Bayesian reconstruction for digital breast tomosynthesis using a non-local Gaussian Markov random field a priori model. 2019, Anais.. Bellingham, CA, USA: SPIE, 2019. Disponível em: https://doi.org/10.1117/12.2513140. Acesso em: 12 nov. 2024.
APA
Salvadeo, D. H. P., Vimieiro, R. de B., Vieira, M. A. da C., & Maidment, A. D. A. (2019). Bayesian reconstruction for digital breast tomosynthesis using a non-local Gaussian Markov random field a priori model. In Proceedings of SPIE (Vol. 10948). Bellingham, CA, USA: SPIE. doi:10.1117/12.2513140
NLM
Salvadeo DHP, Vimieiro R de B, Vieira MA da C, Maidment ADA. Bayesian reconstruction for digital breast tomosynthesis using a non-local Gaussian Markov random field a priori model [Internet]. Proceedings of SPIE. 2019 ; 10948[citado 2024 nov. 12 ] Available from: https://doi.org/10.1117/12.2513140
Vancouver
Salvadeo DHP, Vimieiro R de B, Vieira MA da C, Maidment ADA. Bayesian reconstruction for digital breast tomosynthesis using a non-local Gaussian Markov random field a priori model [Internet]. Proceedings of SPIE. 2019 ; 10948[citado 2024 nov. 12 ] Available from: https://doi.org/10.1117/12.2513140
Trabalho de evento
PMUs and SCADA measurements in power system state estimation through bayesian inference (2019)
Massignan, Julio Augusto Druzina; London Junior, João Bosco Augusto; Maciel, Carlos Dias; Bessani, Michel; Miranda, Vladimiro
Conference titles: IEEE PowerTech Conference. Unidade: EESC
Subjects: SISTEMAS ELÉTRICOS DE POTÊNCIA, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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ABNT
MASSIGNAN, Julio Augusto Druzina et al. PMUs and SCADA measurements in power system state estimation through bayesian inference. 2019, Anais.. Piscataway, NJ, USA: IEEE, 2019. Disponível em: https://doi.org/10.1109/PTC.2019.8810750. Acesso em: 12 nov. 2024.
APA
Massignan, J. A. D., London Junior, J. B. A., Maciel, C. D., Bessani, M., & Miranda, V. (2019). PMUs and SCADA measurements in power system state estimation through bayesian inference. In . Piscataway, NJ, USA: IEEE. doi:10.1109/PTC.2019.8810750
NLM
Massignan JAD, London Junior JBA, Maciel CD, Bessani M, Miranda V. PMUs and SCADA measurements in power system state estimation through bayesian inference [Internet]. 2019 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/PTC.2019.8810750
Vancouver
Massignan JAD, London Junior JBA, Maciel CD, Bessani M, Miranda V. PMUs and SCADA measurements in power system state estimation through bayesian inference [Internet]. 2019 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/PTC.2019.8810750
Trabalho de evento
Learning to race through coordinate descent bayesian optimisation (2018)
Oliveira, Rafael; Rocha, Fernando H. M.; Ott, Lionel; Guizilini, Vitor; Ramos, Fabio; Grassi Júnior, Valdir
Source: Proceedings. Conference titles: IEEE International Conference on Robotics and Automation - ICRA. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, VEÍCULOS AUTÔNOMOS, ENGENHARIA ELÉTRICA
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ABNT
OLIVEIRA, Rafael et al. Learning to race through coordinate descent bayesian optimisation. 2018, Anais.. Piscataway, NJ, USA: IEEE, 2018. Disponível em: https://doi.org/10.1109/ICRA.2018.8460735. Acesso em: 12 nov. 2024.
APA
Oliveira, R., Rocha, F. H. M., Ott, L., Guizilini, V., Ramos, F., & Grassi Júnior, V. (2018). Learning to race through coordinate descent bayesian optimisation. In Proceedings. Piscataway, NJ, USA: IEEE. doi:10.1109/ICRA.2018.8460735
NLM
Oliveira R, Rocha FHM, Ott L, Guizilini V, Ramos F, Grassi Júnior V. Learning to race through coordinate descent bayesian optimisation [Internet]. Proceedings. 2018 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/ICRA.2018.8460735
Vancouver
Oliveira R, Rocha FHM, Ott L, Guizilini V, Ramos F, Grassi Júnior V. Learning to race through coordinate descent bayesian optimisation [Internet]. Proceedings. 2018 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.1109/ICRA.2018.8460735
Trabalho de evento
Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study (2017)
Santos, Fernando P.; Smagula, Stephen F.; Karim, Helmet; Santini, Tales S.; Aizenstein, Howard J.; Ibrahim, Tamer S.; Maciel, Carlos Dias
Source: Proceedings. Conference titles: International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, RESSONÂNCIA MAGNÉTICA, PROCESSAMENTO DE IMAGENS, ENGENHARIA ELÉTRICA
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ABNT
SANTOS, Fernando P. et al. Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study. 2017, Anais.. Lisboa: SciTePress, 2017. Disponível em: https://doi.org/10.5220/0006151601780184. Acesso em: 12 nov. 2024.
APA
Santos, F. P., Smagula, S. F., Karim, H., Santini, T. S., Aizenstein, H. J., Ibrahim, T. S., & Maciel, C. D. (2017). Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study. In Proceedings. Lisboa: SciTePress. doi:10.5220/0006151601780184
NLM
Santos FP, Smagula SF, Karim H, Santini TS, Aizenstein HJ, Ibrahim TS, Maciel CD. Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study [Internet]. Proceedings. 2017 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.5220/0006151601780184
Vancouver
Santos FP, Smagula SF, Karim H, Santini TS, Aizenstein HJ, Ibrahim TS, Maciel CD. Dynamic bayesian network modeling of hippocampal subfields connectivity with 7T fMRI: a case study [Internet]. Proceedings. 2017 ;[citado 2024 nov. 12 ] Available from: https://doi.org/10.5220/0006151601780184
Trabalho de evento
Construction of a clinical decision support system for dental caries management using bayesian networks (2014)
Bessani, M.; Lins, E. C. C. C.; Delbem, Alexandre Cláudio Botazzo ; Maciel, Carlos Dias
Source: Anais. Conference titles: Congresso Brasileiro de Engenharia Biomédica - CBEB. Unidades: ICMC, EESC
Subjects: ENGENHARIA ELÉTRICA, INFERÊNCIA BAYESIANA, CÁRIE DENTÁRIA, SISTEMAS DE APOIO À DECISÃO
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ABNT
BESSANI, M. et al. Construction of a clinical decision support system for dental caries management using bayesian networks. 2014, Anais.. Rio de Janeiro, RJ: SBEB, 2014. Disponível em: https://repositorio.usp.br/directbitstream/86497514-44c6-461c-8ff2-e843b85b0027/cbeb2014_submission_156.pdf. Acesso em: 12 nov. 2024.
APA
Bessani, M., Lins, E. C. C. C., Delbem, A. C. B., & Maciel, C. D. (2014). Construction of a clinical decision support system for dental caries management using bayesian networks. In Anais. Rio de Janeiro, RJ: SBEB. Recuperado de https://repositorio.usp.br/directbitstream/86497514-44c6-461c-8ff2-e843b85b0027/cbeb2014_submission_156.pdf
NLM
Bessani M, Lins ECCC, Delbem ACB, Maciel CD. Construction of a clinical decision support system for dental caries management using bayesian networks [Internet]. Anais. 2014 ;[citado 2024 nov. 12 ] Available from: https://repositorio.usp.br/directbitstream/86497514-44c6-461c-8ff2-e843b85b0027/cbeb2014_submission_156.pdf
Vancouver
Bessani M, Lins ECCC, Delbem ACB, Maciel CD. Construction of a clinical decision support system for dental caries management using bayesian networks [Internet]. Anais. 2014 ;[citado 2024 nov. 12 ] Available from: https://repositorio.usp.br/directbitstream/86497514-44c6-461c-8ff2-e843b85b0027/cbeb2014_submission_156.pdf

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