• Corpus ID: 239049378

Decision Theoretic Cutoff and ROC Analysis for Bayesian Optimal Group Testing

@article{Sakata2021DecisionTC,
  title={Decision Theoretic Cutoff and ROC Analysis for Bayesian Optimal Group Testing},
  author={Ayaka Sakata and Yoshiyuki Kabashima},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10877}
}
We study the inference problem in the group testing to identify defective items from the perspective of the decision theory. We introduce Bayesian inference and consider the Bayesian optimal setting in which the true generative process of the test results is known. We demonstrate the adequacy of the posterior marginal probability in the Bayesian optimal setting as a diagnostic variable based on the area under the curve (AUC). Using the posterior marginal probability, we derive the general… 

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