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Tese (Doutorado)
Bayesian Spatial Process Models for Activation Patterns in Transcranial Magnetic Stimulation Mapping (2023)
Egbon, Osafu Augustine ; Louzada, Francisco (Orientador)
Unidade: INTER:ICMC-UFSCAR
Subjects: CÓRTEX MOTOR, MAPEAMENTO CEREBRAL, PROCESSOS GAUSSIANOS, ESTATÍSTICAS ESPACIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
EGBON, Osafu Augustine. Bayesian Spatial Process Models for Activation Patterns in Transcranial Magnetic Stimulation Mapping. 2023. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/104/104131/tde-12092023-191817/. Acesso em: 03 nov. 2024.
APA
Egbon, O. A. (2023). Bayesian Spatial Process Models for Activation Patterns in Transcranial Magnetic Stimulation Mapping (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/104/104131/tde-12092023-191817/
NLM
Egbon OA. Bayesian Spatial Process Models for Activation Patterns in Transcranial Magnetic Stimulation Mapping [Internet]. 2023 ;[citado 2024 nov. 03 ] Available from: https://www.teses.usp.br/teses/disponiveis/104/104131/tde-12092023-191817/
Vancouver
Egbon OA. Bayesian Spatial Process Models for Activation Patterns in Transcranial Magnetic Stimulation Mapping [Internet]. 2023 ;[citado 2024 nov. 03 ] Available from: https://www.teses.usp.br/teses/disponiveis/104/104131/tde-12092023-191817/
Artigo de periodico
Inverse Gaussian process model with frailty term in reliability analysis (2021)
Morita, Lia Hanna Martins ; Tomazella, Vera Lucia Damasceno ; Balakrishnan, Narayanaswamy ; Ramos, Pedro Luiz ; Ferreira, Paulo Henrique ; Louzada, Francisco
Source: Quality and Reliability Engineering International. Unidade: ICMC
Subjects: FALHA, PROCESSOS GAUSSIANOS, VEROSSIMILHANÇA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MORITA, Lia Hanna Martins et al. Inverse Gaussian process model with frailty term in reliability analysis. Quality and Reliability Engineering International, v. 37, p. 763-784, 2021Tradução . . Disponível em: https://doi.org/10.1002/qre.2762. Acesso em: 03 nov. 2024.
APA
Morita, L. H. M., Tomazella, V. L. D., Balakrishnan, N., Ramos, P. L., Ferreira, P. H., & Louzada, F. (2021). Inverse Gaussian process model with frailty term in reliability analysis. Quality and Reliability Engineering International, 37, 763-784. doi:10.1002/qre.2762
NLM
Morita LHM, Tomazella VLD, Balakrishnan N, Ramos PL, Ferreira PH, Louzada F. Inverse Gaussian process model with frailty term in reliability analysis [Internet]. Quality and Reliability Engineering International. 2021 ; 37 763-784.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1002/qre.2762
Vancouver
Morita LHM, Tomazella VLD, Balakrishnan N, Ramos PL, Ferreira PH, Louzada F. Inverse Gaussian process model with frailty term in reliability analysis [Internet]. Quality and Reliability Engineering International. 2021 ; 37 763-784.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1002/qre.2762
Artigo de periodico
Alpha skew Gaussian naïve Bayes classifier (2021)
Ara, Anderson ; Louzada, Francisco
Source: International Journal of Information Technology and Decision Making. Unidade: ICMC
Subjects: PROCESSOS GAUSSIANOS, ASSIMETRIA, ANÁLISE DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ARA, Anderson e LOUZADA, Francisco. Alpha skew Gaussian naïve Bayes classifier. International Journal of Information Technology and Decision Making, v. 20, p. 1-22, 2021Tradução . . Disponível em: https://doi.org/10.1142/S0219622021500644. Acesso em: 03 nov. 2024.
APA
Ara, A., & Louzada, F. (2021). Alpha skew Gaussian naïve Bayes classifier. International Journal of Information Technology and Decision Making, 20, 1-22. doi:10.1142/S0219622021500644
NLM
Ara A, Louzada F. Alpha skew Gaussian naïve Bayes classifier [Internet]. International Journal of Information Technology and Decision Making. 2021 ; 20 1-22.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1142/S0219622021500644
Vancouver
Ara A, Louzada F. Alpha skew Gaussian naïve Bayes classifier [Internet]. International Journal of Information Technology and Decision Making. 2021 ; 20 1-22.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1142/S0219622021500644
Artigo de periodico
Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas (2021)
Morita, Lia Hanna Martins ; Tomazella, Vera Lucia Damasceno ; Ferreira, Paulo Henrique ; Ramos, Pedro Luiz ; Balakrishnan, Narayanaswamy ; Louzada, Francisco
Source: Applied Stochastic Models in Business and Industry. Unidade: ICMC
Subjects: INFERÊNCIA BAYESIANA, PROCESSOS GAUSSIANOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MORITA, Lia Hanna Martins et al. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas. Applied Stochastic Models in Business and Industry, v. 37, n. 3, p. 612-627, 2021Tradução . . Disponível em: https://doi.org/10.1002/asmb.2601. Acesso em: 03 nov. 2024.
APA
Morita, L. H. M., Tomazella, V. L. D., Ferreira, P. H., Ramos, P. L., Balakrishnan, N., & Louzada, F. (2021). Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas. Applied Stochastic Models in Business and Industry, 37( 3), 612-627. doi:10.1002/asmb.2601
NLM
Morita LHM, Tomazella VLD, Ferreira PH, Ramos PL, Balakrishnan N, Louzada F. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas [Internet]. Applied Stochastic Models in Business and Industry. 2021 ; 37( 3): 612-627.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1002/asmb.2601
Vancouver
Morita LHM, Tomazella VLD, Ferreira PH, Ramos PL, Balakrishnan N, Louzada F. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas [Internet]. Applied Stochastic Models in Business and Industry. 2021 ; 37( 3): 612-627.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1002/asmb.2601

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