Exact inference in the inequality constrained normal linear regression model
用贝叶斯方法处理不等式约束下的正态线性回归模型，通过蒙特卡洛数值积分计算后验分布，并与其他方法比较，用三个实例展示其可用性。
Abstract Inference in the inequality constrained normal linear regression model is approached as a problem in Bayesian inference, using a prior that is the product of a conventional uninformative distribution and an indicator function representing the inequality constraints. The posterior distribution is calculated using Monte Carlo numerical integration, which leads directly to the evaluation of expected values of functions of interest. This approach is compared with others that have been proposed. Three empirical examples illustrate the utility of the proposed methods using an inexpensive 32‐bit microcomputer.