Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Language: English
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paperback. 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!
Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Language: English
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Condition: New. 1st edition NO-PA16APR2015-KAP.
Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367736950 ISBN 13: 9780367736958
Language: English
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Add to basketPaperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 185.
Published by Taylor & Francis Ltd, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Language: English
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138499986 ISBN 13: 9781138499980
Language: English
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Add to basketPaperback. Condition: Brand New. 236 pages. 9.21x6.14x0.55 inches. In Stock.
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Add to basketPaperback. Condition: Brand New. 217 pages. 8.25x5.50x0.50 inches. In Stock.
Published by Chapman and Hall/CRC 2018-12-07, 2018
ISBN 10: 1138499994 ISBN 13: 9781138499997
Language: English
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Add to basketCondition: New. This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some ti.
Published by Chapman and Hall/CRC, 2018
ISBN 10: 1138499994 ISBN 13: 9781138499997
Language: English
Seller: Majestic Books, Hounslow, United Kingdom
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Add to basketBuch. Condition: Neu. Neuware - This work addresses classification using mixture models broadly. Unlike traditional treatments of the subject that heavily focus on unsupervised approaches, this book gives attention to unsupervised, semi-supervised, and supervised classification paradigms. Case studies illustrate both non-Gaussian and Gaussian approaches to model selection.
Published by Chapman and Hall/CRC, 2018
ISBN 10: 1138499994 ISBN 13: 9781138499997
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
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Published by Taylor & Francis Ltd, 2018
ISBN 10: 1138499994 ISBN 13: 9781138499997
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 168.
Published by Chapman and Hall/CRC, 2018
ISBN 10: 1138499994 ISBN 13: 9781138499997
Language: English
Seller: Biblios, Frankfurt am main, HESSE, Germany
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Published by Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 0367736950 ISBN 13: 9780367736958
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -'This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative, modern reference in the mixture modeling literature.' (Douglas Steinley, University of Missouri)Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a clusterPaul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models. 236 pp. Englisch.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some ti.
Seller: moluna, Greven, Germany
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. Peter Tait is a Ph.D. student at the Department of.
Published by Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 0367736950 ISBN 13: 9780367736958
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a resource that I am certain will be used by researchers in statistics and related disciplines for quite some time. The discussion of mixtures with heavy tails and asymmetric distributions will place this text as the authoritative, modern reference in the mixture modeling literature.' (Douglas Steinley, University of Missouri)Mixture Model-Based Classification is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a clusterPaul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. His research focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has published extensively within the field. He is an associate editor for several journals and has served as a guest editor for a number of special issues on mixture models.
Seller: moluna, Greven, Germany
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics. Peter Tait is a Ph.D. student at the Department of.