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Volume 5, Number 1, 2015, Pages 38-51                                                                DOI:10.11948/2015004
Bayesian modeling of forestry data with R using optimization and simulation tools
Yasmin Khan,Md Tanwir Akhtar,Romana Shehla,Athar Ali Khan
Keywords:Bayesian inference, optim, LaplacesDemon, sampling importance resampling, LaplaceApproximation, model comparison.
      Data generated in forestry biometrics are not normal in statistical sense as they rarely follow the normal regression model. Hence, there is a need to develop models and methods in forest biometric applications for non-normal models. Due to generality of Bayesian methods it can be implemented in the situations when Gaussian regression models do not fit the data. Data on diameter at breast height (dbh), which is a very important characteristic in forestry has been fitted to Weibull and gamma models in Bayesian paradigm and comparisons have also been made with its classical counterpart. It may be noted that MCMC simulation tools are used in this study. An attempt has been made to apply Bayesian simulation tools using \textbf{R} software.
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