Sampling-Based Approaches to Calculating Marginal Densities

Alan E. Gelfand; A. F. M. Smith · Journal of the American Statistical Association · 1990

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Alan E. Gelfand; A. F. M. Smith

Publisher

Journal of the American Statistical Association

Year

1990

Language

English

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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.

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APA 7

Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-Based Approaches to Calculating Marginal Densities. https://doi.org/10.1080/01621459.1990.10476213

MLA

Gelfand, Alan E, and A. F. M. Smith. "Sampling-Based Approaches to Calculating Marginal Densities." 1990. https://doi.org/10.1080/01621459.1990.10476213.

Chicago

Gelfand, Alan E. and A. F. M. Smith. 1990. "Sampling-Based Approaches to Calculating Marginal Densities.". https://doi.org/10.1080/01621459.1990.10476213.

Harvard

Gelfand, A. E. and Smith, A. F. M. 1990, Sampling-Based Approaches to Calculating Marginal Densities, Journal of the American Statistical Association, available at: https://doi.org/10.1080/01621459.1990.10476213 [Accessed 7 Aug. 2026].

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Title: Sampling-Based Approaches to Calculating Marginal Densities

Author / contributors: Alan E. Gelfand; A. F. M. Smith

Publisher: Journal of the American Statistical Association

Publication year: 1990

Language: English

Subjects

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Gibbs sampling; Resampling; Sampling (signal processing); Monte Carlo method; Bayesian probability; Importance sampling; Mathematics; Statistics; Marginal distribution; Computer science; Random variable; Filter (signal processing)