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Sampling-Based Approaches to Calculating Marginal Densities
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Sampling-Based Approaches to Calculating Marginal Densities
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Sampling-Based Approaches to Calculating Marginal Densities
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Sampling-Based Approaches to Calculating Marginal Densities
Sampling-Based Approaches to Calculating Marginal Densities
Journal Article

Sampling-Based Approaches to Calculating Marginal Densities

1990
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Overview
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.