Large Deviations Analysis to the Performance of Distributed Detection: Neyman-Pearson and Bayes errors - Brossura
Sinossi
This book studies the performance of distributed detection systems by means of large deviation techniques under two distinct models. In the first model, the error performance is investigated as the number of sensors tends to infinity by assuming that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. It is found that when the second moment of the post-quantization log-likelihood ratio is unbounded, the Neyman-Pearson error exponent becomes a function of the test level; whereas the Bayes error exponent remains unaffected. Also shown is that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents but extends to the actual Bayes errors up to a multiplicative constant. In the second model, the null and alternative distributions become spatially correlated Gaussian, differing in the mean. The issue considered includes whether contiguous marginal likelihood ratio quantizers are optimal. It is shown that this is not true in general, and a sufficient condition is obtained under the case of a single observation per sensor.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
L'autore
Po-Ning Chen received his Ph.D. from University of Maryland, College Park, U.S.A. in 1994. He jointed the National Chiao-Tung University, Taiwan as an associate professor in 1996 and later became a full professor in 2001. His research areas include information and coding theory, large deviation theory, distributed detection and sensor networks.
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Large Deviations Analysis to the Performance of Distributed Detection
Editore:
LAP LAMBERT Academic Publishing Okt 2010, 2010
ISBN 10: 3843369992
ISBN 13: 9783843369992
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book studies the performance of distributed detection systems by means of large deviation techniques under two distinct models. In the first model, the error performance is investigated as the number of sensors tends to infinity by assuming that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. It is found that when the second moment of the post-quantization log-likelihood ratio is unbounded, the Neyman-Pearson error exponent becomes a function of the test level; whereas the Bayes error exponent remains unaffected. Also shown is that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents but extends to the actual Bayes errors up to a multiplicative constant. In the second model, the null and alternative distributions become spatially correlated Gaussian, differing in the mean. The issue considered includes whether contiguous marginal likelihood ratio quantizers are optimal. It is shown that this is not true in general, and a sufficient condition is obtained under the case of a single observation per sensor. 132 pp. Englisch. Codice articolo 9783843369992
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Large Deviations Analysis to the Performance of Distributed Detection
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843369992
ISBN 13: 9783843369992
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chen Po-NingPo-Ning Chen received his Ph.D. from University of Maryland, College Park, U.S.A. in 1994. He jointed the National Chiao-Tung University, Taiwan as an associate professor in 1996 and later became a full professor in 200. Codice articolo 5466921
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Large Deviations Analysis to the Performance of Distributed Detection
Editore:
LAP LAMBERT Academic Publishing Okt 2010, 2010
ISBN 10: 3843369992
ISBN 13: 9783843369992
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book studies the performance of distributed detection systems by means of large deviation techniques under two distinct models. In the first model, the error performance is investigated as the number of sensors tends to infinity by assuming that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. It is found that when the second moment of the post-quantization log-likelihood ratio is unbounded, the Neyman-Pearson error exponent becomes a function of the test level; whereas the Bayes error exponent remains unaffected. Also shown is that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents but extends to the actual Bayes errors up to a multiplicative constant. In the second model, the null and alternative distributions become spatially correlated Gaussian, differing in the mean. The issue considered includes whether contiguous marginal likelihood ratio quantizers are optimal. It is shown that this is not true in general, and a sufficient condition is obtained under the case of a single observation per sensor.Books on Demand GmbH, Überseering 33, 22297 Hamburg 132 pp. Englisch. Codice articolo 9783843369992
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Large Deviations Analysis to the Performance of Distributed Detection : Neyman-Pearson and Bayes errors
Editore:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843369992
ISBN 13: 9783843369992
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book studies the performance of distributed detection systems by means of large deviation techniques under two distinct models. In the first model, the error performance is investigated as the number of sensors tends to infinity by assuming that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for binary hypothesis testing. It is found that when the second moment of the post-quantization log-likelihood ratio is unbounded, the Neyman-Pearson error exponent becomes a function of the test level; whereas the Bayes error exponent remains unaffected. Also shown is that in Bayes testing, the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents but extends to the actual Bayes errors up to a multiplicative constant. In the second model, the null and alternative distributions become spatially correlated Gaussian, differing in the mean. The issue considered includes whether contiguous marginal likelihood ratio quantizers are optimal. It is shown that this is not true in general, and a sufficient condition is obtained under the case of a single observation per sensor. Codice articolo 9783843369992
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Large Deviations Analysis to the Performance of Distributed Detection | Neyman-Pearson and Bayes errors
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843369992
ISBN 13: 9783843369992
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Taschenbuch. Condizione: Neu. Large Deviations Analysis to the Performance of Distributed Detection | Neyman-Pearson and Bayes errors | Po-Ning Chen | Taschenbuch | 132 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843369992 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Codice articolo 107239054
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