A Bayesian method for classification and discrimination
We discuss Bayesian analyses of traditional normal‐mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal‐mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second‐level uncertainties in classification. Copyright © 1992 Statistical Society of Canada
Duke Scholars
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- Statistics & Probability
- 1403 Econometrics
- 0104 Statistics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- 1403 Econometrics
- 0104 Statistics