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Information Complexity Statistical Modeling by Rissanen Jorma (27 results)
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Information and Complexity in Statistical Modeling (Information Science and Statistics)
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Add to baskethardcover. Condition: Good. slightly bent boards, pages clean. text bound upside-down from the cover.
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Condition: New. pp. 158 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.
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Information and Complexity in Statistical Modeling (Information Science and Statistics)
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Information and Complexity in Statistical Modeling
Published by Springer New York, Springer US, 2010
ISBN 10: 1441922679 ISBN 13: 9781441922670
Language: English
£ 50.16
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - No statistical model is 'true' or 'false,' 'right' or 'wrong'; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines 'noise' as the incompressible part in the data without useful information.Such a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few.
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Information and Complexity in Statistical Modeling
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Paperback. Condition: Brand New. 144 pages. 9.00x6.00x0.35 inches. In Stock.
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£ 59.82
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Add to basketGebunden. Condition: New. The author is a distinguished scientist in information theory and statistical modelingThis volume presents a different, yet logically unassailable, view of statistical modeling. It details a method of modeling based on the principle that t.
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Information and Complexity in Statistical Modeling
Published by Springer New York Jan 2007, 2007
ISBN 10: 0387366105 ISBN 13: 9780387366104
Language: English
£ 72.63
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Add to basketBuch. Condition: Neu. Neuware - No statistical model is 'true' or 'false,' 'right' or 'wrong'; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines 'noise' as the incompressible part in the data without useful information.Such a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few.
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Information and Complexity in Statistical Modeling
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Information and Complexity in Statistical Modeling (Information Science and Statistics)
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Information and Complexity in Statistical Modeling
Published by Springer-Verlag New York Inc., 2007
ISBN 10: 0387366105 ISBN 13: 9780387366104
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 432.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The author is a distinguished scientist in information theory and statistical modelingThis volume presents a different, yet logically unassailable, view of statistical modeling. It details a method of modeling based on the principle that the o.
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Information and Complexity in Statistical Modeling
Published by Springer-Verlag New York Inc., 2010
ISBN 10: 1441922679 ISBN 13: 9781441922670
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 254.
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Information and Complexity in Statistical Modeling
Published by Springer New York, Springer US Nov 2010, 2010
ISBN 10: 1441922679 ISBN 13: 9781441922670
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -No statistical model is 'true' or 'false,' 'right' or 'wrong'; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines 'noise' as the incompressible part in the data without useful information.Such a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 152 pp. Englisch.
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Information and Complexity in Statistical Modeling
Published by Springer New York Nov 2010, 2010
ISBN 10: 1441922679 ISBN 13: 9781441922670
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 94.20
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -No statistical model is 'true' or 'false,' 'right' or 'wrong'; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. 152 pp. Englisch.
