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(More customer reviews)Mixture models have become a hot topic in statistics. After you read this book, you will know why.
"Finite Mixture models" have come a long way from classic finite mixture distribution as discused e.g. Titterington et al(1985). A small sample should almost surely entice your taste, with hot items such as hierarchical mixtures-of-experts models, mixtures of GLMs, mixture models for failure-time data, EM algorithms for large data sets, and hidden Markov models. The book gives a lucid overview of recent developments on mixture models since 1990 (the aim of this book in the first place). It expounds on the modern viewpoint that mixtures can be usefully exploited as a mechanism for building flexible statistical models for complex processes, e.g. nonparametric Bayesian models. Balanced attention is given to all three modern approaches to fitting mixture models which include speed-up EM, Bayesian, and stochastic simulation. The whole book is superbly written, and very entertaining---It's hard to put it down once started. It is very update with 45 pages of references and an appendix listing available softwares.
I'm a big fan of Prof. McLachlan's books; and I believe, this latest book of his with one of his student D. Peel, should add another masterpeiece to the long list of marvelous statistics books coming out of Australia and New Zealand...
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An up-to-date, comprehensive account of major issues in finite mixture modelingThis 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:* Provides more than 800 references-40% published since 1995* Includes an appendix listing available mixture software* Links statistical literature with machine learning and pattern recognition literature* Contains more than 100 helpful graphs, charts, and tablesFinite 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.
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