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Statistical Inductive Inference Minimum by Wallace C S (32 results)
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Hardcover. 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 (Information Science and Statistics)
Book 3 of 20: Information Science and StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Book 3 of 20: Information Science and StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Statistical And Inductive Inference By Minimum Message Length
Book 3 of 20: Information Science and StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Taschenbuch. Condition: 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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Taschenbuch. Condition: 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
Book 3 of 20: Information Science and StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Statistical and Inductive Inference by Minimum Message Length
Book 3 of 20: Information Science and StatisticsPublished by Springer-Verlag New York Inc., US, 2005
ISBN 10: 038723795X ISBN 13: 9780387237954
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Hardback. Condition: 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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Gebunden. Condition: 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
Book 3 of 20: Information Science and StatisticsPublished by Springer-Verlag New York Inc., US, 2005
ISBN 10: 038723795X ISBN 13: 9780387237954
Language: English
US$ 248.54
US$ 85.12 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: 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)
Published by Springer, 2005
ISBN 10: 0071457453 ISBN 13: 9780071457453
Hardcover. Condition: 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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Hardcover. Condition: New. ISBN:9780387237954.
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Statistical and Inductive Inference by Minimum Message Length
Book 3 of 20: Information Science and StatisticsPublished by Springer New York Dez 2010, 2010
ISBN 10: 1441920153 ISBN 13: 9781441920157
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
