Abstract
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.
Keywords
Gibbs samplingResamplingSampling (signal processing)Monte Carlo methodBayesian probabilityImportance samplingMathematicsStatisticsMarginal distributionComputer scienceRandom variable
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Publication Info
- Year
- 1990
- Type
- article
- Volume
- 85
- Issue
- 410
- Pages
- 398-409
- Citations
- 6602
- Access
- Closed
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Cite This
Alan E. Gelfand,
A. F. M. Smith
(1990).
Sampling-Based Approaches to Calculating Marginal Densities.
Journal of the American Statistical Association
, 85
(410)
, 398-409.
https://doi.org/10.1080/01621459.1990.10476213
Identifiers
- DOI
- 10.1080/01621459.1990.10476213