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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days. 675.
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Hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Editore: A K Peters/CRC Press 2010-08-09, 2010
ISBN 10: 1568815794 ISBN 13: 9781568815794
Lingua: Inglese
Da: Chiron Media, Wallingford, Regno Unito
EUR 89,78
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 112,54
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals.
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EUR 169,05
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Aggiungi al carrelloHardcover. Condizione: Brand New. 407 pages. 9.25x6.00x1.00 inches. In Stock.
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EUR 124,92
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Aggiungi al carrelloHardcover. Condizione: Brand New. 407 pages. 9.25x6.00x1.00 inches. In Stock. This item is printed on demand.
Da: preigu, Osnabrück, Germania
EUR 137,85
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Aggiungi al carrelloBuch. Condizione: Neu. Pattern Theory | The Stochastic Analysis of Real-World Signals | David Mumford (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2010 | A K Peters/CRC Press | EAN 9781568815794 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 175,08
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Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is an introduction to pattern theory, the theory behind the task of analyzing types of signals that the real world presents to us. It deals with generating mathematical models of the patterns in those signals and algorithms for analyzing the data based on these models. It exemplifies the view of applied mathematics as starting with a collection of problems from some area of science and then seeking the appropriate mathematics for clarifying the experimental data and the underlying processes of producing these data. An emphasis is placed on finding the mathematical and, where needed, computational tools needed to reach those goals, actively involving the reader in this process. Among other examples and problems, the following areas are treated: music as a realvalued function of continuous time, character recognition, the decomposition of an image into regions with distinct colors and textures, facial recognition, and scaling effects present in natural images caused by their statistical selfsimilarity.