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Titlebook: Response Models for Detection of Change; Amnon Rapoport,William E. Stein,Graham J. Burkheim Book 1979 Springer Science+Business Media Dord

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書目名稱Response Models for Detection of Change
編輯Amnon Rapoport,William E. Stein,Graham J. Burkheim
視頻videohttp://file.papertrans.cn/829/828690/828690.mp4
叢書名稱Theory and Decision Library
圖書封面Titlebook: Response Models for Detection of Change;  Amnon Rapoport,William E. Stein,Graham J. Burkheim Book 1979 Springer Science+Business Media Dord
描述This book reports our research on detection of change processes that underlie psychophysical, learning, medical diagnosis, military, and pro- duction control situations, and share three major features. First, the states of the process are not directly observable but become gradually known with the sequential acquisition of fallible information over time. Second, the mechanism that generates the fallible information is not stationary; rather, it is subjected to a sudden and irrevocable change. Thirdly, in- complete, probabilistic information about the time of change is available when the process commences. The purpose of the book is to characterize this class of detection of change processes, to derive the optimal policy that minimizes total expected loss, and, most importantly, to develop testable response models, based on simple decision rules, for describing detection of change behavior. The book is theoretical in the sense that it offers mathematical models of multi-stage decision behavior and solutions to optimization problems. However, it is not anti-empirical, as it aims to stimulate new experimental research and to generate applications. Throughout the book, questions of exp
出版日期Book 1979
關(guān)鍵詞decision theory; evaluation; experiment; information; nature; research; state; statistics
版次1
doihttps://doi.org/10.1007/978-94-009-9386-0
isbn_softcover978-94-009-9388-4
isbn_ebook978-94-009-9386-0
copyrightSpringer Science+Business Media Dordrecht 1979
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The Optimal Policy,rtion of the remainder of this book, in no way exhaust the possible formulations of a wide variety of detection of change processes. But they are simple and tractable, yet sufficiently general to account for a number of two-state detection of change processes and to serve as first order approximatio
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A Response Model with a Fixed Probability Boundary, Given the problem parameters α, .(.), ., and ., the value of ?* (or ?*) may be obtained numerically together with the minimum expected loss .(.) (or .(.)). The optimal policy may then be tested with data collected in detection of change experiments, as done in Chapter 8. If the major purpose of suc
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A Response Model with a Fixed Number of Successive Observations,especially if the DM does not keep a record of the values of the observations already taken or if the observation intervals are very short. This, for example, is the case in the psychophysical experiment to be discussed in Chapter 8. To reduce the DM’s cognitive load, or perhaps better account for t
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Extensions, loosely characterized as follows. A DM observes a sequence oi random variables .(.):. ? ., where .(.) denotes the random variable observed at time . and . ? . It is assumed that .(.) has a known distribution function .(.), which depends on the unknown state at time . Additionally, it is assumed tha
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