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System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles (SpringerBriefs in Mathematics) - Softcover
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This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications.
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System Identification Using Regular and Quantized Observations
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System Identification Using Regular and Quantized Observations
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications. 108 pp. Englisch. Seller Inventory # 9781461462910
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System Identification Using Regular and Quantized Observations
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regularFirst book devoted to large deviations to system identificationApplication orientedPresents. Seller Inventory # 4199027
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System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles
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System Identification Using Regular and Quantized Observations
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Springer New York, Springer New York Feb 2013, 2013
ISBN 10: 1461462916
ISBN 13: 9781461462910
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Taschenbuch. Condition: Neu. Neuware -¿This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 108 pp. Englisch. Seller Inventory # 9781461462910
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications. Seller Inventory # 9781461462910
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System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles (SpringerBriefs in Mathematics)
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System Identification Using Regular and Quantized Observations : Applications of Large Deviations Principles
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