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二元与连续变量混合情形下的预测判别与分类AI 解读

We derive the predictive allocation rule for classification of observations involving mixtures of binary and continuous variables. Our approach is based on the usual frequency distributions of the location model and vague prior distributions for the unknown parameters. The same approach is used to derive predictive rules when low-order linear models are imposed on the population parameters. We also present Monte Carlo results on the performance of the new predictive rule and…

二元与连续混合变量的预测判别及分类AI 解读

We derive the predictive allocation rule for classification of observations involving mixtures of binary and continuous variables. Our approach is based on the usual frequency distributions of the location model and vague prior distributions for the unknown parameters. The same approach is used to derive predictive rules when low-order linear models are imposed on the population parameters. We also present Monte Carlo results on the performance of the new predictive rule and…