（序贯）联合检测与估计：经典结果与新方向
(Sequential) Joint Detection and Estimation: Classic Results and New Directions
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中文总结 AI 辅助
本文概述联合检测与估计问题，介绍其经典方法、序贯框架下的相关方法，通过数值示例说明最优方法的优势，并探讨该领域的开放问题与未来方向。
中文摘要 AI 辅助
本文概述了联合检验两个假设并估计所选模型参数的问题，这类问题广泛存在于各类应用场景中。首先，我们对联合检测与估计的次优及最优方法进行概念性介绍，并通过数值示例说明最优方法相较于次优方法的优势。接下来，我们探讨更复杂的问题表述如何影响前述结果。第二部分讨论序贯框架下的联合检测与估计：先通过序贯假设检验介绍序贯分析，再探讨联合检测与估计的次优及最优序贯方法，数值示例表明最优序贯方法相较于次优方法及固定样本量的最优序贯方法具有优势。第三部分阐述联合检测与估计领域的开放问题及未来研究方向。
英文摘要
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.
