Sampling-Based Approaches to Calculating Marginal Densities
综述了随机替代、吉布斯采样和采样重要性重抽样三种基于抽样的边际概率分布数值估计方法，比较了它们在常见联合概率结构下的适用性，并讨论了在贝叶斯后验密度计算中的应用。
Abstract Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions. The three approaches will be reviewed, compared, and contrasted in relation to various joint probability structures frequently encountered in applications. In particular, the relevance of the approaches to calculating Bayesian posterior densities for a variety of structured models will be discussed and illustrated.