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Artigo de periodico
Expert-based maps and highly detailed surface drainage models to support digital soil mapping (2021)
Mello, Fellipe Alcântara de Oliveira; Demattê, José Alexandre Melo ; Rizzo, Rodnei ; Dotto, André Carnieletto ; Poppiel, Raul Roberto ; Mendes, Wanderson de Sousa ; Guimarães, Clécia Cristina Barbosa
Source: Geoderma. Unidades: ESALQ, FMRP
Subjects: DRENAGEM, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO, MODELOS PARA PROCESSOS ESTOCÁSTICOS, RELEVO, SISTEMAS ESPECIALISTAS
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ABNT
MELLO, Fellipe Alcântara de Oliveira et al. Expert-based maps and highly detailed surface drainage models to support digital soil mapping. Geoderma, v. 384, p. 1-15, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.geoderma.2020.114779. Acesso em: 23 nov. 2025.
APA
Mello, F. A. de O., Demattê, J. A. M., Rizzo, R., Dotto, A. C., Poppiel, R. R., Mendes, W. de S., & Guimarães, C. C. B. (2021). Expert-based maps and highly detailed surface drainage models to support digital soil mapping. Geoderma, 384, 1-15. doi:10.1016/j.geoderma.2020.114779
NLM
Mello FA de O, Demattê JAM, Rizzo R, Dotto AC, Poppiel RR, Mendes W de S, Guimarães CCB. Expert-based maps and highly detailed surface drainage models to support digital soil mapping [Internet]. Geoderma. 2021 ; 384 1-15.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.geoderma.2020.114779
Vancouver
Mello FA de O, Demattê JAM, Rizzo R, Dotto AC, Poppiel RR, Mendes W de S, Guimarães CCB. Expert-based maps and highly detailed surface drainage models to support digital soil mapping [Internet]. Geoderma. 2021 ; 384 1-15.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.geoderma.2020.114779
Artigo de periodico
Thirty years of credal networks: specification, algorithms and complexity (2020)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, PROBABILIDADE
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ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Thirty years of credal networks: specification, algorithms and complexity. International Journal of Approximate Reasoning, v. 126, p. 133-157, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.08.009. Acesso em: 23 nov. 2025.
APA
Mauá, D. D., & Cozman, F. G. (2020). Thirty years of credal networks: specification, algorithms and complexity. International Journal of Approximate Reasoning, 126, 133-157. doi:10.1016/j.ijar.2020.08.009
NLM
Mauá DD, Cozman FG. Thirty years of credal networks: specification, algorithms and complexity [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 133-157.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Vancouver
Mauá DD, Cozman FG. Thirty years of credal networks: specification, algorithms and complexity [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 133-157.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Artigo de periodico
Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks (2020)
Villanueva Llerena, Julissa ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
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ABNT
VILLANUEVA LLERENA, Julissa e MAUÁ, Denis Deratani. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks. International Journal of Approximate Reasoning, v. 126, p. 158-180-, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.07.008. Acesso em: 23 nov. 2025.
APA
Villanueva Llerena, J., & Mauá, D. D. (2020). Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks. International Journal of Approximate Reasoning, 126, 158-180-. doi:10.1016/j.ijar.2020.07.008
NLM
Villanueva Llerena J, Mauá DD. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 158-180-.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.008
Vancouver
Villanueva Llerena J, Mauá DD. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 158-180-.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.008
Artigo de periodico
A generalized transition model for grouped longitudinal categorical data (2020)
Lara, Idemauro Antonio Rodrigues de ; Moral, Rafael A; Taconeli, Cesar A; Reigada, Carolina; Hinde, John
Source: Biometrical Journal. Unidade: ESALQ
Subjects: ANÁLISE DE DADOS LONGITUDINAIS, DADOS CATEGORIZADOS, DISTRIBUIÇÕES (PROBABILIDADE), INSETOS VETORES, MODELOS PARA PROCESSOS ESTOCÁSTICOS, SUÍNOS
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ABNT
LARA, Idemauro Antonio Rodrigues de et al. A generalized transition model for grouped longitudinal categorical data. Biometrical Journal, p. 1-21, 2020Tradução . . Disponível em: https://doi.org/10.1002/bimj.201900394. Acesso em: 23 nov. 2025.
APA
Lara, I. A. R. de, Moral, R. A., Taconeli, C. A., Reigada, C., & Hinde, J. (2020). A generalized transition model for grouped longitudinal categorical data. Biometrical Journal, 1-21. doi:10.1002/bimj.201900394
NLM
Lara IAR de, Moral RA, Taconeli CA, Reigada C, Hinde J. A generalized transition model for grouped longitudinal categorical data [Internet]. Biometrical Journal. 2020 ; 1-21.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1002/bimj.201900394
Vancouver
Lara IAR de, Moral RA, Taconeli CA, Reigada C, Hinde J. A generalized transition model for grouped longitudinal categorical data [Internet]. Biometrical Journal. 2020 ; 1-21.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1002/bimj.201900394
Artigo de periodico
Estimating the interaction graph of stochastic neural dynamics (2019)
Duarte, Aline ; Galves, Antonio ; Löcherbach, Eva ; Ost, Guilherme
Source: Bernoulli. Unidade: IME
Subjects: BIOESTATÍSTICA, MODELOS PARA PROCESSOS ESTOCÁSTICOS
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ABNT
DUARTE, Aline et al. Estimating the interaction graph of stochastic neural dynamics. Bernoulli, v. 25, n. 1, p. 771-792, 2019Tradução . . Disponível em: https://doi.org/10.3150/17-bej1006. Acesso em: 23 nov. 2025.
APA
Duarte, A., Galves, A., Löcherbach, E., & Ost, G. (2019). Estimating the interaction graph of stochastic neural dynamics. Bernoulli, 25( 1), 771-792. doi:10.3150/17-bej1006
NLM
Duarte A, Galves A, Löcherbach E, Ost G. Estimating the interaction graph of stochastic neural dynamics [Internet]. Bernoulli. 2019 ; 25( 1): 771-792.[citado 2025 nov. 23 ] Available from: https://doi.org/10.3150/17-bej1006
Vancouver
Duarte A, Galves A, Löcherbach E, Ost G. Estimating the interaction graph of stochastic neural dynamics [Internet]. Bernoulli. 2019 ; 25( 1): 771-792.[citado 2025 nov. 23 ] Available from: https://doi.org/10.3150/17-bej1006
Artigo de periodico
RNA polymerase interactions and elongation rate (2019)
Belitsky, Vladimir ; Schutz, G. M
Source: Journal of Theoretical Biology. Unidade: IME
Subjects: RNA POLIMERASES, MODELOS PARA PROCESSOS ESTOCÁSTICOS
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ABNT
BELITSKY, Vladimir e SCHUTZ, G. M. RNA polymerase interactions and elongation rate. Journal of Theoretical Biology, v. 462, p. 370-380, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.jtbi.2018.11.025. Acesso em: 23 nov. 2025.
APA
Belitsky, V., & Schutz, G. M. (2019). RNA polymerase interactions and elongation rate. Journal of Theoretical Biology, 462, 370-380. doi:10.1016/j.jtbi.2018.11.025
NLM
Belitsky V, Schutz GM. RNA polymerase interactions and elongation rate [Internet]. Journal of Theoretical Biology. 2019 ; 462 370-380.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.jtbi.2018.11.025
Vancouver
Belitsky V, Schutz GM. RNA polymerase interactions and elongation rate [Internet]. Journal of Theoretical Biology. 2019 ; 462 370-380.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.jtbi.2018.11.025
Artigo de periodico
Stochastic estimation of potential and depleted productivity of soybean grain and oil (2019)
Alambert, Marcelo Rodrigues; Umburanas, Renan Caldas; Schwerz, Felipe; Reichardt, Klaus; Dourado-Neto, Durval
Source: International Journal of Plant Production. Unidade: ESALQ
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, ÓLEO DE SOJA, SOJA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALAMBERT, Marcelo Rodrigues et al. Stochastic estimation of potential and depleted productivity of soybean grain and oil. International Journal of Plant Production, p. 1-14, 2019Tradução . . Disponível em: https://doi.org/10.1007/s42106-019-00042-y. Acesso em: 23 nov. 2025.
APA
Alambert, M. R., Umburanas, R. C., Schwerz, F., Reichardt, K., & Dourado-Neto, D. (2019). Stochastic estimation of potential and depleted productivity of soybean grain and oil. International Journal of Plant Production, 1-14. doi:10.1007/s42106-019-00042-y
NLM
Alambert MR, Umburanas RC, Schwerz F, Reichardt K, Dourado-Neto D. Stochastic estimation of potential and depleted productivity of soybean grain and oil [Internet]. International Journal of Plant Production. 2019 ; 1-14.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s42106-019-00042-y
Vancouver
Alambert MR, Umburanas RC, Schwerz F, Reichardt K, Dourado-Neto D. Stochastic estimation of potential and depleted productivity of soybean grain and oil [Internet]. International Journal of Plant Production. 2019 ; 1-14.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1007/s42106-019-00042-y
Artigo de periodico
Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks (2018)
Pena, Rodrigo Felipe de Oliveira; Vellmer, Sebastian; Bernardi, Davide; Roque, Antônio Carlos; Lindner, Benjamin
Source: Frontiers in Computational Neuroscience. Unidade: FFCLRP
Subjects: REDES COMPLEXAS, REDE NERVOSA, REDES NEURAIS, MODELOS PARA PROCESSOS ESTOCÁSTICOS
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ABNT
PENA, Rodrigo Felipe de Oliveira et al. Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks. Frontiers in Computational Neuroscience, v. 12, 2018Tradução . . Disponível em: https://doi.org/10.3389/fncom.2018.00009. Acesso em: 23 nov. 2025.
APA
Pena, R. F. de O., Vellmer, S., Bernardi, D., Roque, A. C., & Lindner, B. (2018). Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks. Frontiers in Computational Neuroscience, 12. doi:10.3389/fncom.2018.00009
NLM
Pena RF de O, Vellmer S, Bernardi D, Roque AC, Lindner B. Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks [Internet]. Frontiers in Computational Neuroscience. 2018 ; 12[citado 2025 nov. 23 ] Available from: https://doi.org/10.3389/fncom.2018.00009
Vancouver
Pena RF de O, Vellmer S, Bernardi D, Roque AC, Lindner B. Self-consistent scheme for spike-train power spectra in heterogeneous sparse networks [Internet]. Frontiers in Computational Neuroscience. 2018 ; 12[citado 2025 nov. 23 ] Available from: https://doi.org/10.3389/fncom.2018.00009
Artigo de periodico
Robustifying sum-product networks (2018)
Mauá, Denis Deratani ; Conaty, Diarmaid; Cozman, Fabio Gagliardi ; Poppenhaeger, Katja; Campos, Cassio Polpo de
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Subjects: INTELIGÊNCIA ARTIFICIAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani et al. Robustifying sum-product networks. International Journal of Approximate Reasoning, v. 101, p. 163-180, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2018.07.003. Acesso em: 23 nov. 2025.
APA
Mauá, D. D., Conaty, D., Cozman, F. G., Poppenhaeger, K., & Campos, C. P. de. (2018). Robustifying sum-product networks. International Journal of Approximate Reasoning, 101, 163-180. doi:10.1016/j.ijar.2018.07.003
NLM
Mauá DD, Conaty D, Cozman FG, Poppenhaeger K, Campos CP de. Robustifying sum-product networks [Internet]. International Journal of Approximate Reasoning. 2018 ; 101 163-180.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003
Vancouver
Mauá DD, Conaty D, Cozman FG, Poppenhaeger K, Campos CP de. Robustifying sum-product networks [Internet]. International Journal of Approximate Reasoning. 2018 ; 101 163-180.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003
Trabalho de evento-anais periodico
Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming (2017)
Bueno, Thiago Pereira; Mauá, Denis Deratani ; Barros, Leliane Nunes de ; Cozman, Fabio Gagliardi
Source: PMLR: Proceedings of Machine Learning Research. Conference titles: International Symposium on Imprecise Probability: Theories and Applications - ISIPTA. Unidades: IME, EP
Subjects: PROCESSOS DE MARKOV, PROGRAMAÇÃO LÓGICA, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BUENO, Thiago Pereira et al. Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming. PMLR: Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: http://proceedings.mlr.press/v62/bueno17a.html. Acesso em: 23 nov. 2025. , 2017
APA
Bueno, T. P., Mauá, D. D., Barros, L. N. de, & Cozman, F. G. (2017). Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming. PMLR: Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de http://proceedings.mlr.press/v62/bueno17a.html
NLM
Bueno TP, Mauá DD, Barros LN de, Cozman FG. Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming [Internet]. PMLR: Proceedings of Machine Learning Research. 2017 ;( 62): 49-60.[citado 2025 nov. 23 ] Available from: http://proceedings.mlr.press/v62/bueno17a.html
Vancouver
Bueno TP, Mauá DD, Barros LN de, Cozman FG. Modeling Markov decision processes with imprecise probabilities using probabilistic logic programming [Internet]. PMLR: Proceedings of Machine Learning Research. 2017 ;( 62): 49-60.[citado 2025 nov. 23 ] Available from: http://proceedings.mlr.press/v62/bueno17a.html

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