论文标题
用于学习和适应COVID-19数据的决策算法
Decision-Making Algorithms for Learning and Adaptation with Application to COVID-19 Data
论文作者
论文摘要
这项工作着重于适应和学习的新型决策算法系列的开发，这些算法是专门针对决策问题的，并且是通过基于决策理论的第一原则来构建的。一个关键的观察结果是，估计和决策问题在结构上是不同的，因此，在针对决策问题调整时，证明对前者成功的算法不需要表现良好。我们提出了一种新方案，称为BLLR（屏障对数可能比率算法），并证明了其适用于意大利Covid-19的大流行中的真实数据。结果说明了设计工具跟踪疫情不同阶段的能力。
This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for decision problems. We propose a new scheme, referred to as BLLR (barrier log-likelihood ratio algorithm) and demonstrate its applicability to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak.
