Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling
本文回顾并演示了吉布斯采样在正态数据模型中计算贝叶斯边际后验和预测密度的方法，涵盖方差成分、均值排序、层次生长曲线和交叉试验缺失数据等案例，适合需要实现贝叶斯推断的统计和计量研究者。
Abstract The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial. In all cases the approach is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries.