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Finite Mixture Models

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 by means of 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 for use in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to a few of these problems. The writer also considers how the EM algorithm can also be scaled to care for the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
* Provides more than 800 references-40% published since 1995
* Includes an appendix listing to be had mixture software
* Links statistical literature with machine learning and pattern recognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is the most important resource for both applied and theoretical statisticians in addition to for researchers in the many areas in which finite mixture models can be utilized to analyze data.

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