Filtros : "Milan, Luis Aparecido" "Saraiva, Erlandson Ferreira" Removido: "IME-MAE" Limpar

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  • Unidades: ICMC, IME, INTER: ICMC -UFSCAR

    Subjects: PROBABILIDADE, PROCESSOS ESTOCÁSTICOS

    Versão PublicadaAcesso à fonteHow to cite
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    • ABNT

      SARAIVA, Erlandson Ferreira et al. An integrated approach for making inference on the number of clusters in a mixture model. Tradução . Basel: MDPI, 2021. . Disponível em: https://www.mdpi.com/1099-4300/22/12/1438/pdf. Acesso em: 28 ago. 2024.
    • APA

      Saraiva, E. F., Suzuki, A. K., Milan, L. A., & Pereira, C. A. de B. (2021). An integrated approach for making inference on the number of clusters in a mixture model. In . Basel: MDPI. Recuperado de https://www.mdpi.com/1099-4300/22/12/1438/pdf
    • NLM

      Saraiva EF, Suzuki AK, Milan LA, Pereira CA de B. An integrated approach for making inference on the number of clusters in a mixture model [Internet]. Basel: MDPI; 2021. [citado 2024 ago. 28 ] Available from: https://www.mdpi.com/1099-4300/22/12/1438/pdf
    • Vancouver

      Saraiva EF, Suzuki AK, Milan LA, Pereira CA de B. An integrated approach for making inference on the number of clusters in a mixture model [Internet]. Basel: MDPI; 2021. [citado 2024 ago. 28 ] Available from: https://www.mdpi.com/1099-4300/22/12/1438/pdf
  • Source: Brazilian Journal of Probability and Statistics. Unidade: ICMC

    Subjects: DISTRIBUIÇÕES (PROBABILIDADE), INFERÊNCIA BAYESIANA, AMOSTRAGEM, MÉTODO DE MONTE CARLO

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    • 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
  • Source: Entropy. Unidades: ICMC, IME, INTER: ICMC -UFSCAR

    Subjects: PROBABILIDADE, PROCESSOS ESTOCÁSTICOS

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      SARAIVA, Erlandson Ferreira et al. An integrated approach for making inference on the number of clusters in a mixture model. Entropy, v. 21, n. 11, p. 1-18, 2019Tradução . . Disponível em: https://doi.org/10.3390/e21111063. Acesso em: 28 ago. 2024.
    • APA

      Saraiva, E. F., Suzuki, A. K., Milan, L. A., & Pereira, C. A. de B. (2019). An integrated approach for making inference on the number of clusters in a mixture model. Entropy, 21( 11), 1-18. doi:10.3390/e21111063
    • NLM

      Saraiva EF, Suzuki AK, Milan LA, Pereira CA de B. An integrated approach for making inference on the number of clusters in a mixture model [Internet]. Entropy. 2019 ; 21( 11): 1-18.[citado 2024 ago. 28 ] Available from: https://doi.org/10.3390/e21111063
    • Vancouver

      Saraiva EF, Suzuki AK, Milan LA, Pereira CA de B. An integrated approach for making inference on the number of clusters in a mixture model [Internet]. Entropy. 2019 ; 21( 11): 1-18.[citado 2024 ago. 28 ] Available from: https://doi.org/10.3390/e21111063
  • Source: Entropy. Unidade: ICMC

    Subjects: INFERÊNCIA BAYESIANA, MÉTODOS MCMC

    Acesso à fonteDOIHow to cite
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    • 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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