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Artigo de periodico
Bayesian criterion for identification of differentially expressed genes (2021)
Saraiva, Erlandson F ; Milan, Luis Aparecido ; Pereira, Carlos Alberto de Bragança
Source: Communications in Statistics: Case Studies, Data Analysis and Applications. Unidade: IME
Subjects: INFERÊNCIA BAYESIANA, EXPRESSÃO GÊNICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, Erlandson F e MILAN, Luis Aparecido e PEREIRA, Carlos Alberto de Bragança. Bayesian criterion for identification of differentially expressed genes. Communications in Statistics: Case Studies, Data Analysis and Applications, v. 7, n. 1, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.1080/23737484.2020.1800535. Acesso em: 03 dez. 2025.
APA
Saraiva, E. F., Milan, L. A., & Pereira, C. A. de B. (2021). Bayesian criterion for identification of differentially expressed genes. Communications in Statistics: Case Studies, Data Analysis and Applications, 7( 1), 1-14. doi:10.1080/23737484.2020.1800535
NLM
Saraiva EF, Milan LA, Pereira CA de B. Bayesian criterion for identification of differentially expressed genes [Internet]. Communications in Statistics: Case Studies, Data Analysis and Applications. 2021 ; 7( 1): 1-14.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1080/23737484.2020.1800535
Vancouver
Saraiva EF, Milan LA, Pereira CA de B. Bayesian criterion for identification of differentially expressed genes [Internet]. Communications in Statistics: Case Studies, Data Analysis and Applications. 2021 ; 7( 1): 1-14.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1080/23737484.2020.1800535
Artigo de periodico
A predictive Bayes factor approach to identify genes differentially expressed: an application to Escherichia coli bacterium data (2014)
Louzada, Francisco ; Saraiva, Erlandson F; Milan, Luis; Cobre, Juliana
Source: Brazilian Journal of Probability and Statistics. Unidade: ICMC
Assunto: INFERÊNCIA BAYESIANA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOUZADA, Francisco et al. A predictive Bayes factor approach to identify genes differentially expressed: an application to Escherichia coli bacterium data. Brazilian Journal of Probability and Statistics, v. 28, n. 2, p. 167-189, 2014Tradução . . Disponível em: https://doi.org/10.1214/12-bjps200. Acesso em: 03 dez. 2025.
APA
Louzada, F., Saraiva, E. F., Milan, L., & Cobre, J. (2014). A predictive Bayes factor approach to identify genes differentially expressed: an application to Escherichia coli bacterium data. Brazilian Journal of Probability and Statistics, 28( 2), 167-189. doi:10.1214/12-bjps200
NLM
Louzada F, Saraiva EF, Milan L, Cobre J. A predictive Bayes factor approach to identify genes differentially expressed: an application to Escherichia coli bacterium data [Internet]. Brazilian Journal of Probability and Statistics. 2014 ; 28( 2): 167-189.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1214/12-bjps200
Vancouver
Louzada F, Saraiva EF, Milan L, Cobre J. A predictive Bayes factor approach to identify genes differentially expressed: an application to Escherichia coli bacterium data [Internet]. Brazilian Journal of Probability and Statistics. 2014 ; 28( 2): 167-189.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1214/12-bjps200
Artigo de periodico
Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm (2014)
Saraiva, Erlandson F; Louzada, Francisco ; Milan, Luis
Source: Applied Mathematics and Computation. Unidade: ICMC
Subjects: INFERÊNCIA BAYESIANA, INFERÊNCIA ESTATÍSTICA, ESTATÍSTICA APLICADA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, Erlandson F e LOUZADA, Francisco e MILAN, Luis. Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm. Applied Mathematics and Computation, v. 244, p. 959-975, 2014Tradução . . Disponível em: https://doi.org/10.1016/j.amc.2014.07.032. Acesso em: 03 dez. 2025.
APA
Saraiva, E. F., Louzada, F., & Milan, L. (2014). Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm. Applied Mathematics and Computation, 244, 959-975. doi:10.1016/j.amc.2014.07.032
NLM
Saraiva EF, Louzada F, Milan L. Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm [Internet]. Applied Mathematics and Computation. 2014 ; 244 959-975.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1016/j.amc.2014.07.032
Vancouver
Saraiva EF, Louzada F, Milan L. Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm [Internet]. Applied Mathematics and Computation. 2014 ; 244 959-975.[citado 2025 dez. 03 ] Available from: https://doi.org/10.1016/j.amc.2014.07.032

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