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Decision-making algorithms for learning and adaptation with application to COVID-19 data.

Stefano Marano1, Ali H Sayed2

  • 1DIEM, University of Salerno, via Giovanni Paolo II 132, Fisciano SA, I-84084, Italy.

Signal Processing
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces novel decision-making algorithms for adaptation and learning, distinct from estimation methods. A new barrier log-likelihood ratio (BLLR) test effectively tracked COVID-19 pandemic phases in Italy.

Keywords:
COVID-19 PandemicDecision systemsLMS AlgorithmLearning and adaptation

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Area of Science:

  • Decision theory
  • Machine learning
  • Statistical signal processing

Background:

  • Estimation and decision problems are structurally different.
  • Existing algorithms successful for estimation may not suit decision problems.
  • Adaptation and learning require tailored decision-making approaches.

Purpose of the Study:

  • Develop a new family of decision-making algorithms based on first principles of decision theory.
  • Address the structural differences between estimation and decision problems.
  • Introduce a novel algorithm applicable to real-world dynamic scenarios.

Main Methods:

  • Utilized classical tools from quickest detection theory.
  • Proposed a tailored version of Page's test, named the barrier log-likelihood ratio (BLLR) test.
  • Applied the BLLR test to real-world data from the COVID-19 pandemic in Italy.

Main Results:

  • Demonstrated the BLLR test's applicability to real-world data.
  • Successfully tracked different phases of the COVID-19 outbreak using the developed algorithm.
  • Highlighted the effectiveness of decision-theory-based algorithms for dynamic adaptation.

Conclusions:

  • The proposed BLLR test is a viable tool for decision-making in adaptive systems.
  • Algorithms built on decision theory principles offer advantages over adapted estimation algorithms.
  • The developed methods show promise for analyzing and managing real-world dynamic processes like pandemics.