Abstract
Abstract Even to the initiated, statistical calculations based on Bayes's Theorem can be daunting because of the numerical integrations required in all but the simplest applications. Moreover, from a teaching perspective, introductions to Bayesian statistics—if they are given at all—are circumscribed by these apparent calculational difficulties. Here we offer a straightforward sampling-resampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily implemented calculation strategies. Key Words: Bayesian inferenceExploratory data analysisGraphical methodsInfluencePosterior distributionPredictionPrior distributionRandom variate generationSampling-resampling techniquesSensitivity analysisWeighted bootstrap
Keywords
StatisticsBayesian probabilityPerspective (graphical)ResamplingSampling (signal processing)Bayesian statisticsEconometricsMathematicsBayesian inferenceComputer scienceArtificial intelligence
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Publication Info
- Year
- 1992
- Type
- article
- Volume
- 46
- Issue
- 2
- Pages
- 84-88
- Citations
- 905
- Access
- Closed
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Cite This
A. F. M. Smith,
Alan E. Gelfand
(1992).
Bayesian Statistics without Tears: A Sampling–Resampling Perspective.
The American Statistician
, 46
(2)
, 84-88.
https://doi.org/10.1080/00031305.1992.10475856
Identifiers
- DOI
- 10.1080/00031305.1992.10475856