Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).
翻译:考虑到贝叶斯建模的灵活性和适用性,在这项工作中,我们在回归环境下修订两个等级模型的主要特征;研究模型的完全概率结构以及每个模型参数的完全有条件分布;在分级扩展下,我们允许模型第二阶段的平均值对一组共变体有线性依赖;充分描述和推算了在与模型相匹配时用于获取样品的Gibbs抽样的抽样算法;此外,我们考虑一项案例研究,其中将植物大小定性为氮土壤浓度和组合系数(农场)的函数。