A hierarchical Bayesian regression framework to analyze climate data from Central Asia region (2024)
- Authors:
- USP affiliated authors: ACHCAR, JORGE ALBERTO - FMRP ; OLIVEIRA, RICARDO PUZIOL DE - FMRP ; BARILI, EMERSON - FMRP
- Unidade: FMRP
- DOI: 10.21608/ejec.2024.381182
- Subjects: CLIMA; TEMPERATURA ATMOSFÉRICA; REGRESSÃO LINEAR; INFERÊNCIA BAYESIANA
- Keywords: Climate data; Multiple linear regression models; Bayesian inference; MCMC métodos; Minimum and maximum yearly average temperatures
- Language: Inglês
- Imprenta:
- Source:
- Título: The Egyptian Journal of Environmental Change
- ISSN: 2090-6005
- Volume/Número/Paginação/Ano: v. 16, n. 2, p. 31-50, 2024
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
-
ABNT
BARILI, Emerson e ACHCAR, Jorge Alberto e OLIVEIRA, Ricardo Puziol de. A hierarchical Bayesian regression framework to analyze climate data from Central Asia region. The Egyptian Journal of Environmental Change, v. 16, n. 2, p. 31-50, 2024Tradução . . Disponível em: https://doi.org/10.21608/ejec.2024.381182. Acesso em: 27 dez. 2025. -
APA
Barili, E., Achcar, J. A., & Oliveira, R. P. de. (2024). A hierarchical Bayesian regression framework to analyze climate data from Central Asia region. The Egyptian Journal of Environmental Change, 16( 2), 31-50. doi:10.21608/ejec.2024.381182 -
NLM
Barili E, Achcar JA, Oliveira RP de. A hierarchical Bayesian regression framework to analyze climate data from Central Asia region [Internet]. The Egyptian Journal of Environmental Change. 2024 ; 16( 2): 31-50.[citado 2025 dez. 27 ] Available from: https://doi.org/10.21608/ejec.2024.381182 -
Vancouver
Barili E, Achcar JA, Oliveira RP de. A hierarchical Bayesian regression framework to analyze climate data from Central Asia region [Internet]. The Egyptian Journal of Environmental Change. 2024 ; 16( 2): 31-50.[citado 2025 dez. 27 ] Available from: https://doi.org/10.21608/ejec.2024.381182 - Occurrence of railway accidents related to some personal and professional train conductor factors: use of statistical models in the presence of excess of zeros
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Informações sobre o DOI: 10.21608/ejec.2024.381182 (Fonte: oaDOI API)
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