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Mixture Model-Based Classification (Hardcover) (Paul D. McNicholas)

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This book details mixture model-based approaches to the clustering and classification of unlabelled observations. Chapters discuss Gaussian mixtures, mixtures of factor analyzers and their extensions, variable selection, and high-dimensional applications, as well as mixtures of distributions that parameterize concentration; mixtures of skewed distributions; mixtures of distributions that parameterize skewness and concentration, or tail weight; and mixtures of multiple scaled distributions. They also cover methods for clustering and classification of longitudinal data, cluster-weighted models, averaging mixture models, the definition of a cluster, and the existence of a best clustering and classification method. Annotation ©2016 Ringgold, Inc., Portland, OR (protoview.com)
Number of Pages: 212
Genre: Mathematics
Sub-Genre: Probability + Statistics / General
Format: Hardcover
Publisher: Taylor & Francis
Author: Paul D. McNicholas
Language: English
Street Date: August 19, 2016
TCIN: 50373834
UPC: 9781482225662
Item Number (DPCI): 248-06-2523
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