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Aggiungi al carrelloCondizione: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9781584883180.
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Da: Majestic Books, Hounslow, Regno Unito
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Aggiungi al carrelloCondizione: New. pp. 352.
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 352.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 352 pages. 9.21x6.14x0.79 inches. In Stock.
Da: DeckleEdge LLC, Albuquerque, NM, U.S.A.
hardcover. Condizione: new.
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Multivariate Bayesian Statistics | Models for Source Separation and Signal Unmixing | Daniel B. Rowe | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | Taylor & Francis | EAN 9780367454661 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 182,11
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Aggiungi al carrelloPaperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Chapman And Hall Crc, 2020
Da: Books in my Basket, New Delhi, India
EUR 81,47
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Aggiungi al carrelloN.A. Condizione: New. ISBN:9780367413347.
Lingua: Inglese
Editore: Taylor & Francis, Chapman And Hall/CRC, 2019
ISBN 10: 0367454661 ISBN 13: 9780367454661
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the 'cocktail-party' analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters.Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many 'cocktail party' problems they may confront in practice. 350 pp. Englisch.
Da: moluna, Greven, Germania
EUR 84,19
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Daniel B. Rowe holds a joint appointment as an assistant professor of Biophysics and Biostatistics at the Medical College of Wisconsin, Milwaukee, Wisconsin, USA.Of the two primary approaches to the classic source separation problem, only one doe.
Lingua: Inglese
Editore: Taylor & Francis, Chapman And Hall/CRC, 2019
ISBN 10: 0367454661 ISBN 13: 9780367454661
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 92,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but also allow inferences to be drawn from them.Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing offers a thorough, self-contained treatment of the source separation problem. After an introduction to the problem using the 'cocktail-party' analogy, Part I provides the statistical background needed for the Bayesian source separation model. Part II considers the instantaneous constant mixing models, where the observed vectors and unobserved sources are independent over time but allowed to be dependent within each vector. Part III details more general models in which sources can be delayed, mixing coefficients can change over time, and observation and source vectors can be correlated over time. For each model discussed, the author gives two distinct ways to estimate the parameters.Real-world source separation problems, encountered in disciplines from engineering and computer science to economics and image processing, are more difficult than they appear. This book furnishes the fundamental statistical material and up-to-date research results that enable readers to understand and apply Bayesian methods to help solve the many 'cocktail party' problems they may confront in practice.