Problem
Research questions and friction points this paper is trying to address.
joint detection and estimation
hypotheses testing
sequential analysis
(Sequential) Joint Detection and Estimation: Classic Results and New Directions
🤖 AI Summary
本文探讨了联合检测与估计问题，通过比较最优与次优方法，并引入序列分析框架，展示了最优方法在不同场景下的优势。
📝 Abstract
We provide an overview of the problem of jointly testing two hypotheses and estimating a parameter of the selected model. Such problems arise in a variety of applications. First, we present a conceptual introduction to suboptimal and optimal procedures for joint detection and estimation. A numerical example illustrates the advantages of the optimal procedure over suboptimal ones. Next, we discuss how more advanced problem formulations affect the presented results. The second part covers joint detection and estimation in a sequential framework. First, we provide an introduction to sequential analysis through sequential hypothesis testing. Then, suboptimal and optimal sequential procedures for joint detection and estimation are discussed. A numerical example shows the advantages of optimal sequential procedures over suboptimal and optimal sequential procedures with a fixed number of samples. The third part discusses open problems and future research directions in joint detection and estimation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Joint Detection and Estimation
Sequential Analysis
Optimal Procedures
Suboptimal Procedures
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