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1
Partitioning gene expression data by data-driven Markov chain Monte Carlo
Partitioning gene expression data by data-driven Markov chain Monte Carlo
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Partitioning gene expression data by data-driven Markov chain Monte Carlo

E. F Saraiva (Erlandson Ferreira Saraiva) Adriano Kamimura Suzuki; Francisco Louzada; Luis Milan (Luis Aparecido Milan)

Journal of Applied Statistics London : Taylor and Francis v. 43, n. 6, p. 1155-1173, 2016

London 2016

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD 2753469 )(Acessar)

2
Bayesian computational methods for estimation of two-parameters Weibull distribution in presence of right-censored data
Bayesian computational methods for estimation of two-parameters Weibull distribution in presence of right-censored data
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Artigo 		Adicionar ao Meu Espaço

Bayesian computational methods for estimation of two-parameters Weibull distribution in presence of right-censored data

Erlandson F. Saraiva (Erlandson Ferreira Saraiva) Adriano Kamimura Suzuki

Chilean Journal of Statistics Valparaiso : Sociedad Chilena de Estadística v. 8, n. 2, p. 25-43, Set. 2017

Valparaíso 2017

Acesso online. A biblioteca também possui exemplares impressos.

3
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
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
Material Type:
Artigo 		Adicionar ao Meu Espaço

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

Erlandson Ferreira Saraiva Adriano Kamimura Suzuki; Luis Aparecido Milan

Entropy Basel : MDPI v. 20, n. 9, art. 642, p. 1-21, 2018

Basel 2018

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD 2900999 )(Acessar)

4
A Bayesian sparse finite mixture model for clustering data from a heterogeneous population
A Bayesian sparse finite mixture model for clustering data from a heterogeneous population
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A Bayesian sparse finite mixture model for clustering data from a heterogeneous population

Erlandson Ferreira Saraiva Adriano Kamimura Suzuki; Luis Aparecido Milan https://orcid.org/0000-0001-6466-7865

Brazilian Journal of Probability and Statistics São Paulo : ABE v. 34, n. 2, p. 323-344

São Paulo 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD 2940800 )(Acessar)

5
Identifying differentially expressed genes using the Polya urn scheme
Identifying differentially expressed genes using the Polya urn scheme
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Identifying differentially expressed genes using the Polya urn scheme

Erlandson Ferreira Saraiva Luís Aparecido Milan; Adriano Kamimura Suzuki

Communications for Statistical Applications and Methods Seoul, Korea : Korean Statistical Society (KSS), and Korean International Statistical Society v. 24, n. 6, p. 627-640, 2017

Seoul, Korea 2017

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD 2870605 )(Acessar)

6
A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling
A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling
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A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling

Erlandson Ferreira Saraiva C. A. B Pereira; Adriano Kamimura Suzuki

Journal of Statistical Computation and Simulation Abingdon : Taylor & Francis v. 89, n. 15, p. 2848-2870, 2019

Abingdon 2019

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-2954292 ) e outros locais(Acessar)

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