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Published by Taylor & Francis Group, 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
Language: English
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367736950 ISBN 13: 9780367736958
Language: English
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Published by Taylor & Francis Group, 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
Language: English
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Published by Taylor & Francis Group, 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
Language: English
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Add to basketPaperback. Condition: Brand New. 236 pages. 9.21x6.14x0.55 inches. In Stock.
Published by Apple Academic Press Inc., 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
Language: English
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Add to basketHardcover. Condition: Brand New. 212 pages. 9.25x6.25x0.75 inches. In Stock.
Published by Chapman and Hall/CRC, 2016
ISBN 10: 1482225662 ISBN 13: 9781482225662
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.
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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 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.
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.