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Abstract

Generalized linear models offer convenient and highly applicable tools for modeling and predicting the behavior of random variables in terms of observable factors and covariates. This paper investigates applications of a special case of generalized linear model to improve the accuracy of predictions and decisions adopting Bayesian methods, in the specific context of assessing coronary artery disease. The basic model is developed for this application using binary response. The results clearly demonstrate the potential advantages offered by this approach.

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