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  • 标题:B2Z: R Package for Bayesian Two-Zone Models
  • 本地全文:下载
  • 作者:João Vitor Dias Monteiro ; Sudipto Banerjee ; Gurumurthy Ramachandran
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2011
  • 卷号:43
  • 期号:1
  • 页码:1-23
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要:A primary issue in industrial hygiene is the estimation of a worker's exposure to chemical, physical and biological agents. Mathematical modeling is increasingly being used as a method for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Recently, Zhang, Banerjee, Yang, Lungu, and Ramachandran (2009) proposed Bayesian hierarchical models for estimating parameters and exposure concentrations for the two-zone differential equation models and for predicting concentrations in a zone near and far away from the source of contamination. Bayesian estimation, however, can often require substantial amounts of user-defined code and tuning. In this paper, we introduce a statistical software package, B2Z , built upon the R statistical computing platform that implements a Bayesian model for estimating model parameters and exposure concentrations in two-zone models. We discuss the algorithms behind our package and illustrate its use with simulated and real data examples.
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