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A compensatory approach to optimal selection with mastery scores

机译:掌握分数的最优选择的一种补偿方法

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摘要

A Bayesian approach for simultaneous optimization of test-based decisions is presented using the example of a selection decision for a treatment followed by a mastery decision. A distinction is made between weak and strong rules where, as opposed to strong rules, weak rules use prior test scores as collateral data. Conditions for monotonicity of optimal weak and strong rules are presented. It is shown that under mild conditions on the test score distributions and utility functions, weak rules are always compensatory by nature.
机译:使用选择决策后再掌握决策的示例,提出了一种同时优化基于测试的决策的贝叶斯方法。区分弱规则和强规则,与强规则相反,弱规则使用先前的测试分数作为抵押数据。给出了最优弱规则和强规则的单调性的条件。结果表明,在温和的条件下,对测验分数分布和效用函数,弱规则始终是自然的补偿。

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