Sampling-Based Approaches to Calculating Marginal Densities
Sampling-Based Approaches to Calculating Marginal Densities
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DOI：
10.1080/01621459.1990.10476213
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发表时间：
1990-06
影响因子：
3.7
通讯作者：
A. Gelfand;Adrian F. M. Smith
中科院分区：
文献类型：
--
作者：
A. Gelfand;Adrian F. M. Smith
摘要随机替代、吉布斯抽样和抽样重要性恢复算法可以看作是三种基于抽样（或蒙特卡罗）的方法来计算边际概率分布的数值估计。这三种方法将被审查，比较，并在应用中经常遇到的各种联合概率结构进行对比。特别是，相关的方法来计算贝叶斯后验密度的各种结构化模型将进行讨论和说明。
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.