Bayesian classification based on multivariate binary data

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1994 (EN)
Bayesian classification based on multivariate binary data (EN)

Kokolakis, GE (EN)
Johnson, WO (EN)

N/A (EN)

Consider a disease which has associated with it d symptoms that are either present or absent. Several specific symptoms are known for an individual. The question is whether the person has the disease? This is a classification problem based on multivariate binary data. Our approach is Bayesian and involves the prediction of future d-vectors of binary responses. Underlying this problem is the implicit estimation of the corresponding 2d cell probabilities. This is difficult with low structure and with moderate or large d, unless the sample sizes for the training data are enormous. Our model incorporates a prior distribution on unknown parameters, and a 'smoothing' parameter that relates the cells to one another. The posterior is approximated in order to obtain cell probability estimates and an approximate predictive density. Consistency results are indicated, and the procedure is illustrated with data involving the diagnosis of a disease called 'dry eyes'. © 1994. (EN)


predictive density (EN)
entropy (EN)
kernel estimate (EN)
Dirichlet distribution (EN)

Εθνικό Μετσόβιο Πολυτεχνείο (EL)
National Technical University of Athens (EN)

Journal of Statistical Planning and Inference (EN)



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