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Artigo de periodico
Partitioning gene expression data by data-driven Markov chain Monte Carlo (2016)
Saraiva, E. F; Suzuki, Adriano Kamimura ; Louzada, Francisco ; Milan, Luis
Source: Journal of Applied Statistics. Unidade: ICMC
Subjects: PROBABILIDADE, INFERÊNCIA BAYESIANA, INFERÊNCIA PARAMÉTRICA, INFERÊNCIA ESTATÍSTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, E. F et al. Partitioning gene expression data by data-driven Markov chain Monte Carlo. Journal of Applied Statistics, v. 43, n. 6, p. 1155-1173, 2016Tradução . . Disponível em: https://doi.org/10.1080/02664763.2015.1092113. Acesso em: 12 ago. 2024.
APA
Saraiva, E. F., Suzuki, A. K., Louzada, F., & Milan, L. (2016). Partitioning gene expression data by data-driven Markov chain Monte Carlo. Journal of Applied Statistics, 43( 6), 1155-1173. doi:10.1080/02664763.2015.1092113
NLM
Saraiva EF, Suzuki AK, Louzada F, Milan L. Partitioning gene expression data by data-driven Markov chain Monte Carlo [Internet]. Journal of Applied Statistics. 2016 ; 43( 6): 1155-1173.[citado 2024 ago. 12 ] Available from: https://doi.org/10.1080/02664763.2015.1092113
Vancouver
Saraiva EF, Suzuki AK, Louzada F, Milan L. Partitioning gene expression data by data-driven Markov chain Monte Carlo [Internet]. Journal of Applied Statistics. 2016 ; 43( 6): 1155-1173.[citado 2024 ago. 12 ] Available from: https://doi.org/10.1080/02664763.2015.1092113
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: 12 ago. 2024.
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 2024 ago. 12 ] 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 2024 ago. 12 ] 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: 12 ago. 2024.
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 2024 ago. 12 ] 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 2024 ago. 12 ] Available from: https://doi.org/10.1016/j.amc.2014.07.032

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