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Titlebook: An Introduction to Signal Detection and Estimation; H. Vincent Poor Textbook 19881st edition Springer Science+Business Media New York 1988

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樓主
發(fā)表于 2025-3-21 16:12:54 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱An Introduction to Signal Detection and Estimation
影響因子2023H. Vincent Poor
視頻videohttp://file.papertrans.cn/156/155478/155478.mp4
學科分類Springer Texts in Electrical Engineering
圖書封面Titlebook: An Introduction to Signal Detection and Estimation;  H. Vincent Poor Textbook 19881st edition Springer Science+Business Media New York 1988
影響因子The purpose of this book is to introduce the reader to the basic theory of signal detection and estimation. It is assumed that the reader has a working knowledge of applied probabil- ity and random processes such as that taught in a typical first-semester graduate engineering course on these subjects. This material is covered, for example, in the book by Wong (1983) in this series. More advanced concepts in these areas are introduced where needed, primarily in Chapters VI and VII, where continuous-time problems are treated. This book is adapted from a one-semester, second-tier graduate course taught at the University of Illinois. However, this material can also be used for a shorter or first-tier course by restricting coverage to Chapters I through V, which for the most part can be read with a background of only the basics of applied probability, including random vectors and conditional expectations. Sufficient background for the latter option is given for exam pIe in the book by Thomas (1986), also in this series.
Pindex Textbook 19881st edition
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https://doi.org/10.1007/978-3-662-28861-0plications we are interested in the related problem of estimating dynamic or time-varying parameters. In the traditional terminology, a dynamic parameter is usually called a ., so the latter problem is known as . or ..
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https://doi.org/10.1007/978-3-642-94448-2random observation .. In many situations arising in practice we are interested not in making a choice between two (or among several) discrete situations, but rather in making a choice among a continuum of possible states of nature. In particular, as in the composite hypothesis testing problems discu
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https://doi.org/10.1007/978-3-662-28861-0ither a set of vectors or is a discrete set. Throughout this analysis a key role was played by a family of densities {p.; θ ∞ Λ} on the observation space, either through the likelihood ratio in hypothesis testing, through the computation of an . parameter distribution in Bayesian estimation, or thro
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