Marano Stefano, Sayed Ali H
DIEM, University of Salerno, via Giovanni Paolo II 132, Fisciano SA, I-84084, Italy.
EPFL, School of Engineering, Lausanne, CH-1015, Switzerland.
Signal Processing. 2022 May;194:108426. doi: 10.1016/j.sigpro.2021.108426. Epub 2021 Dec 7.
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 the latter. Exploiting classical tools from quickest detection, we propose a tailored version of Page's test, referred to as BLLR (barrier log-likelihood ratio) test, 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.
这项工作专注于开发一类新的用于适应和学习的决策算法，这些算法是专门针对决策问题量身定制的，并且是基于决策理论的第一性原理构建的。一个关键的观察结果是，估计问题和决策问题在结构上是不同的，因此，那些在前者上已证明成功的算法，在调整用于后者时不一定能表现良好。利用快速检测中的经典工具，我们提出了佩奇检验的一个定制版本，称为BLLR（障碍对数似然比）检验，并证明了其对意大利新冠疫情真实数据的适用性。结果说明了该设计工具追踪疫情不同阶段的能力。
