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Bayesian inference and wind field statistical modeling applied to multiple source estimation.

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第一作者： Roseane A S,Albani

第一单位： Polytechnic Institute, Rio de Janeiro State University, 28.625-570 Nova Friburgo, Brazil. Electronic address: roseanealves75@gmail.com.

作者： Roseane A S,Albani ^[1] ; Vinicius V L,Albani ^[2] ; Luiz E S,Gomes ^[3] ; Helio S,Migon ^[4] ; Antonio J,Silva Neto ^[5]

作者单位： Polytechnic Institute, Rio de Janeiro State University, 28.625-570 Nova Friburgo, Brazil. Electronic address: roseanealves75@gmail.com. ^[1] Dept of Mathematics, Federal University of Santa Catarina, Campus Trindade, 88.040-900 Florianopolis, Brazil. ^[2] Institute of Mathematics, Federal University of Rio de Janeiro, 21.941-909 Rio de Janeiro, Brazil. ^[3] Polytechnic Institute, Rio de Janeiro State University, 28.625-570 Nova Friburgo, Brazil; Institute of Mathematics, Federal University of Rio de Janeiro, 21.941-909 Rio de Janeiro, Brazil. ^[4] Polytechnic Institute, Rio de Janeiro State University, 28.625-570 Nova Friburgo, Brazil. ^[5]

关键词 Atmospheric dispersion Bayesian inference MCMC algorithms Metropolis in Gibbs sampler Source estimation Uncertainty quantification

医学主题词贝叶斯定理(Bayes Theorem);风(Wind);模型, 统计学(Models, Statistical);算法(Algorithms);概率(Probability);蒙特卡罗法(Monte Carlo Method)

DOI 10.1016/j.envpol.2023.121061

PMID 36702429

发布时间 2023-02-23

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