The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling
Abstract
The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial. In all cases the approach is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries.
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Citations
Cites
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Self-citationBayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Self-citationParametric empirical Bayes inference: theory and applications
Morris, C. N. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477920 · Cited by 1,448
Estimation in Parallel Randomized Experiments
Rubin, D. B. (1981) · Journal of Educational Statistics · doi:10.3102/10769986006004377 · Cited by 269
Cited by
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Variable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828
Casella, G., & George, E. I. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475878 · Cited by 2,364
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
Self-citationBayesian structural equation modeling: A more flexible representation of substantive theory
Muthén, B., & Asparouhov, T. (2012) · Psychological Methods · doi:10.1037/a0026802 · Cited by 1,562
Metadata
- Authors
- Gelfand, A. E., Hills, S. E., Racine-Poon, A., & Smith, A. F. M.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1990-12
- Volume
- 85
- Issue
- 412
- Pages
- 972-985
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2046811824
- Cited by
- 969
- References
- 6
- FWCI
- 29.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gelfand, A. E., Hills, S. E., Racine-Poon, A., & Smith, A. F. M. (1990). Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1990.10474968
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