Finite Mixture Models Contributor(s): McLachlan, Geoffrey J. (Author), Peel, David (Author) |
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ISBN: 0471006262 ISBN-13: 9780471006268 Publisher: Wiley-Interscience OUR PRICE: $205.15 Product Type: Hardcover - Other Formats Published: October 2000 Annotation: An up-to-date, comprehensive account of major issues in finite mixture modeling This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts. Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data. |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - General |
Dewey: 519.2 |
LCCN: 00-43324 |
Series: Wiley Probability and Statistics |
Physical Information: 1.03" H x 6.46" W x 9.58" (1.69 lbs) 464 pages |
Descriptions, Reviews, Etc. |
Publisher Description: Im hier beschriebenen Modell wird die Verteilung einer Zufallsgrö e als Mischung einer endlichen Zahl von Komponentenverteilungen in verschiedenen Verhältnissen behandelt. Die Verhältnisse sind dabei nichtnegativ und summieren sich zu eins. Typische Einsatzgebiete dieses Ansatzes bestehen in Populationen heterogener Zusammensetzung, beispielsweise bei klinischen Versuchen oder Zuverlässigkeitsprüfungen in der Technik. Die Autoren legen Wert auf die praktischen Aspekte des Verfahrens; einschlägige Software lä t sich aus den Anhängen entnehmen. (12/00) |