Estimating tree height-diameter models with the Bayesian method.
Estimating tree height-diameter models with the Bayesian method.
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用贝叶斯方法估计树高-D直径模型
DOI：
10.1155/2014/683691
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发表时间：
2014
影响因子：
--
通讯作者：
Xiang C
中科院分区：
文献类型：
--
作者：
Zhang X;Duan A;Zhang J;Xiang C
选择6个候选树高-直径模型进行树高-直径关系分析。常用的估计树高-直径模型的方法是基于概率的频率解释的经典（频率论）方法，如非线性最小二乘法（NLS）和最大似然法（ML）。与经典方法相比，贝叶斯方法具有独特的优点，即待估计的参数被视为随机变量。本研究分别采用经典方法和贝叶斯方法对6个树高-直径模型进行了估计。经典方法和贝叶斯方法都表明，威布尔模型是“最好的”模型使用的数据1。此外，基于威布尔模型，data 2被用来比较贝叶斯方法与信息先验与无信息先验和经典方法。结果表明，贝叶斯方法预测精度的提高导致预测值的置信带较经典方法窄，具有信息先验的参数的置信带也较无信息先验和经典方法窄。参数的估计后验分布可以设置为使用data 2估计参数的新先验。
Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist) approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS) and the maximum likelihood method (ML). The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the “best” model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.