Mixture Model-Based Classification

Author:   Paul D. McNicholas
Publisher:   Taylor & Francis Inc
ISBN:  

9781482225662


Pages:   236
Publication Date:   19 August 2016
Format:   Hardback
Availability:   In Print   Availability explained
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Mixture Model-Based Classification


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Author:   Paul D. McNicholas
Publisher:   Taylor & Francis Inc
Imprint:   Chapman & Hall/CRC
Dimensions:   Width: 15.60cm , Height: 2.00cm , Length: 23.40cm
Weight:   0.476kg
ISBN:  

9781482225662


ISBN 10:   1482225662
Pages:   236
Publication Date:   19 August 2016
Audience:   General/trade ,  College/higher education ,  Professional and scholarly ,  General ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Reviews

This Monograph, Mixture Model-Based Classification is an excellent book, highly relevant to every statistician working with classification problems. International Society for Clinical Biostatistics, 2017 This monograph is an extensive introduction of mixture models with applications in classification and clustering. . . The author did good work by organizing the materials in a very natural way as well as presenting methods and algorithms in great detail. Moreover, many case studies help the reader understand and appreciate the methodologies presented. Journal of the American Statistical Association, 2017


Author Information

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. 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.

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