Sampling-Based Approaches to Calculating Marginal Densities

作者：Alan E. Gelfand, A. F. M. Smith · 发表于：Journal of the American Statistical Association · 年份：1990 · DOI：10.2307/2289776 · 被引用次数：1535 · 研究领域：demographic modeling and climate adaptation、Census and Population Estimation、Rural development and sustainability

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.