Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model
扩展了贝叶斯矩分析方法，展示方差参数与回归系数的关系如何生成丰富的后验密度，并介绍预测和模型选择技术，通过实验比较BMOM与贝叶斯方法。
In this article we extend previous BMOM results by showing how information about a variance parameter and its relation to regression coefficients produces a rich class of postdata densities for regression parameters. Prediction and model selection techniques are also described. We also discuss the well‐documented link between cross‐entropy and the average log odds and then use this criterion in an experiment to compare results obtained from BMOM and Bayes approaches using data generated from known models.