论文标题
约翰逊SB和Weibull发行的贝叶斯推断
Bayesian Inference for Johnson's SB and Weibull distributions
论文作者
论文摘要
四参数约翰逊的SB（JSB）和三参数Weibull分布在林业领域受到了很多关注，以表征胸高（DBH）的直径。在这项工作中，我们建议估算JBS分布参数的贝叶斯方法。最大似然方法使用迭代方法，例如Newton-Raphson（NR）算法来最大程度地提高似然函数的对数。但是不能保证NR方法会收敛。通过在本研究中的模拟中验证了用于估计JSB分布参数有时无法收敛的NR方法的事实。此外，结果表明，这项工作中提出的贝叶斯估计量相对于初始值有效，并有效地估算了JSB分布的参数。将这些模型安装到三个地块的DBH数据中时，比较了JSB和三参数Weibull分布的性能，这些图是从107个研究中随机选择的，这些研究是在107个混合时代的Ponderapea pine（Pinus ponderosa dougl。贝叶斯范式表明，当将这些模型拟合到三个图的DBH数据时，JBS比三参数Weibull比三参数Weibull具有优越的模型。
The four-parameter Johnson's SB (JSB) and three-parameter Weibull distributions have received much attention in the field of forestry for characterizing diameters at breast height (DBH). In this work, we suggest the Bayesian method for estimating parameters of the JBS distribution. The maximum likelihood approach uses iterative methods such as Newton-Raphson (NR) algorithm for maximizing the logarithm of the likelihood function. But there is no guarantee that the NR method converges. This fact that the NR method for estimating the parameters of the JSB distribution sometimes fails to converge was verified through simulation in this study. Further, it was shown that the Bayesian estimators presented in this work were robust with respect to the initial values and estimate the parameters of the JSB distribution efficiently. The performance of the JSB and three-parameter Weibull distributions was compared in a Bayesian paradigm when these models were fitted to DBH data of three plots that randomly selected from a study established in 107 plots of mixed-age ponderosa pine (Pinus ponderosa Dougl. ex Laws.) with scattered western junipers at the Malheur National Forest in south end of the Blue Mountains near Burns, Oregon, USA. Bayesian paradigm demonstrated that JBS was superior model than the three-parameter Weibull for characterizing the DBH distribution when these models were fitted to the DBH data of the three plots.
