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Statistical And Inductive Inference By Minimum Message Length (Hb)
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Statistical And Inductive Inference By Minimum Message Length (Hb)
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Statistical And Inductive Inference By Minimum Message Length (Hb)
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical and Inductive Inference by Minimum Message Length.
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Ajouter au panierHardcover. Slight sunning to top edge of front cover and spine, otherwise very good condition. No dustjacket. Part of the Springer Information Science & Statistics series. 429pp. ISBN-10 038723795X ISBN-13: ISBN-13: 9780387237954.
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STATISTICAL AND INDUCTIVE INFERENCE BY MINIMUM MESSAGE LENGTH
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STATISTICAL AND INDUCTIVE INFERENCE BY MINIMUM MESSAGE LENGTH
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STATISTICAL AND INDUCTIVE INFERENCE BY MINIMUM MESSAGE LENGTH
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Edition internationaleSTATISTICAL AND INDUCTIVE INFERENCE BY MINIMUM MESSAGE LENGTH (HB)
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Statistical And Inductive Inference By Minimum Message Length
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical And Inductive Inference By Minimum Message Length
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical And Inductive Inference By Minimum Message Length
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Ajouter au panierTaschenbuch. Etat : Neu. Statistical and Inductive Inference by Minimum Message Length | C. S. Wallace | Taschenbuch | xvi | Englisch | 2010 | Humana | EAN 9781441920157 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Statistical And Inductive Inference By Minimum Message Length
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Statistical and Inductive Inference by Minimum Message Length
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi cation of the sample was a way of brie y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks' arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton's insight, we may never have made the connection between inference and brief encoding, which is the heart of this work.
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Statistical And Inductive Inference By Minimum Message Length
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
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Statistical and Inductive Inference by Minimum Message Length
Edité par Springer-Verlag New York Inc., US, 2005
ISBN 10 : 038723795X ISBN 13 : 9780387237954
Langue: anglais
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Ajouter au panierHardback. Etat : New. 2005 ed. Mythanksareduetothemanypeoplewhohaveassistedintheworkreporte d here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks' arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton's insight, we may never have made the connection between inference and brief encoding, which is the heart of this work.
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Ajouter au panierGebunden. Etat : New. Since 1965, Professor Wallace and others have been developing an approach tostatistical estimation, hypothesis testing, model selection and their applications in the Artificial Intelligence field of Machine LearningMythanksareduetothemanypeoplewh.
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Statistical and Inductive Inference by Minimum Message Length
Edité par Springer-Verlag New York Inc., US, 2005
ISBN 10 : 038723795X ISBN 13 : 9780387237954
Langue: anglais
Vendeur : Rarewaves.com UK, London, Royaume-Uni
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Ajouter au panierHardback. Etat : New. 2005 ed. Mythanksareduetothemanypeoplewhohaveassistedintheworkreporte d here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks' arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton's insight, we may never have made the connection between inference and brief encoding, which is the heart of this work.
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Edité par Springer, 2005
ISBN 10 : 0071457453 ISBN 13 : 9780071457453
Vendeur : HPB-Red, Dallas, TX, Etats-Unis
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Ajouter au panierHardcover. Etat : 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!
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Statistical and Inductive Inference By Minimum Message Length
Edité par Springer, 2005
Vendeur : Books in my Basket, New Delhi, Inde
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Ajouter au panierHardcover. Etat : New. ISBN:9780387237954.
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Statistical and Inductive Inference by Minimum Message Length
Edité par Springer New York Dez 2010, 2010
ISBN 10 : 1441920153 ISBN 13 : 9781441920157
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi cation of the sample was a way of brie y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks' arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton's insight, we may never have made the connection between inference and brief encoding, which is the heart of this work. 448 pp. Englisch.
