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.

"This Monograph, “Mixture Model-Based Classification” is an excellent book, highly relevant to every statistician working with classification problems."~International Society for Clinical Biostatistics"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"I would recommend this book to anyone interested in learning about application of mixture models to classification problems." ~The International Biometric Society

• Mixture Model-Based Classification

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.

"This Monograph, “Mixture Model-Based Classification” is an excellent book, highly relevant to every statistician working with classification problems."~International Society for Clinical Biostatistics"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"I would recommend this book to anyone interested in learning about application of mixture models to classification problems." ~The International Biometric Society

• Mixture Model-Based Classification

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.

"This Monograph, “Mixture Model-Based Classification” is an excellent book, highly relevant to every statistician working with classification problems."~International Society for Clinical Biostatistics"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"I would recommend this book to anyone interested in learning about application of mixture models to classification problems." ~The International Biometric Society

• Mixture Model-Based Classification