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Filtros : "Milan, Luis Aparecido" "Universidade Federal de Mato Grosso do Sul (UFMS)" Removido: "School of Chemistry and Biochemistry, Georgia Institute of Technology, GA" Limpar

Filtros

Artigo de periodico
A Bayesian sparse finite mixture model for clustering data from a heterogeneous population (2020)
Saraiva, Erlandson Ferreira ; Suzuki, Adriano Kamimura ; Milan, Luis Aparecido
Source: Brazilian Journal of Probability and Statistics. Unidade: ICMC
Subjects: DISTRIBUIÇÕES (PROBABILIDADE), INFERÊNCIA BAYESIANA, AMOSTRAGEM, MÉTODO DE MONTE CARLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, Erlandson Ferreira e SUZUKI, Adriano Kamimura e MILAN, Luis Aparecido. A Bayesian sparse finite mixture model for clustering data from a heterogeneous population. Brazilian Journal of Probability and Statistics, v. 34, n. 2, p. 323-344, 2020Tradução . . Disponível em: https://doi.org/10.1214/18-BJPS425. Acesso em: 28 ago. 2024.
APA
Saraiva, E. F., Suzuki, A. K., & Milan, L. A. (2020). A Bayesian sparse finite mixture model for clustering data from a heterogeneous population. Brazilian Journal of Probability and Statistics, 34( 2), 323-344. doi:10.1214/18-BJPS425
NLM
Saraiva EF, Suzuki AK, Milan LA. A Bayesian sparse finite mixture model for clustering data from a heterogeneous population [Internet]. Brazilian Journal of Probability and Statistics. 2020 ; 34( 2): 323-344.[citado 2024 ago. 28 ] Available from: https://doi.org/10.1214/18-BJPS425
Vancouver
Saraiva EF, Suzuki AK, Milan LA. A Bayesian sparse finite mixture model for clustering data from a heterogeneous population [Internet]. Brazilian Journal of Probability and Statistics. 2020 ; 34( 2): 323-344.[citado 2024 ago. 28 ] Available from: https://doi.org/10.1214/18-BJPS425
Artigo de periodico
Bayesian computational methods for sampling from the posterior distribution of a bivariate survival model, based on AMH copula in the presence of right-censored data (2018)
Saraiva, Erlandson Ferreira; Suzuki, Adriano Kamimura ; Milan, Luis Aparecido
Source: Entropy. Unidade: ICMC
Subjects: INFERÊNCIA BAYESIANA, MÉTODOS MCMC
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, Erlandson Ferreira e SUZUKI, Adriano Kamimura e MILAN, Luis Aparecido. Bayesian computational methods for sampling from the posterior distribution of a bivariate survival model, based on AMH copula in the presence of right-censored data. Entropy, v. 20, n. 9, p. 1-21, 2018Tradução . . Disponível em: https://doi.org/10.3390/e20090642. Acesso em: 28 ago. 2024.
APA
Saraiva, E. F., Suzuki, A. K., & Milan, L. A. (2018). Bayesian computational methods for sampling from the posterior distribution of a bivariate survival model, based on AMH copula in the presence of right-censored data. Entropy, 20( 9), 1-21. doi:10.3390/e20090642
NLM
Saraiva EF, Suzuki AK, Milan LA. Bayesian computational methods for sampling from the posterior distribution of a bivariate survival model, based on AMH copula in the presence of right-censored data [Internet]. Entropy. 2018 ; 20( 9): 1-21.[citado 2024 ago. 28 ] Available from: https://doi.org/10.3390/e20090642
Vancouver
Saraiva EF, Suzuki AK, Milan LA. Bayesian computational methods for sampling from the posterior distribution of a bivariate survival model, based on AMH copula in the presence of right-censored data [Internet]. Entropy. 2018 ; 20( 9): 1-21.[citado 2024 ago. 28 ] Available from: https://doi.org/10.3390/e20090642

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