Finite Mixture Models

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John Wiley & Sons, 22.03.2004 - 419 Seiten
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An up-to-date, comprehensive account of major issues in finitemixture modeling
This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts.
Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. 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 patternrecognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
 

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Inhalt

1 General Introduction
1
2 ML Fitting of Mixture Models
40
3 Multivariate Normal Mixtures
81
4 Bayesian Approach to Mixture Analysis
117
5 Mixtures with Nonnormal Components
135
6 Assessing the Number of Components in Mixture Models
175
7 Multivariate t Mixtures
221
8 Mixtures of Factor Analyzers
238
10 Mixture Models for FailureTime Data
268
11 Mixture Analysis of Directional Data
287
12 Variants of the EM Algorithm for Large Databases
302
13 Hidden Markov Models
326
Mixture Software
343
References
349
Author Index
395
Subject Index
407

9 Fitting Mixture Models to Binned Data
257

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Über den Autor (2004)

GEOFFREY McLACHLAN, PhD, DSc, is Professor in the Department ofMathematics at the University of Queensland, Australia.

DAVID PEEL, PhD, is a research fellow in the Department ofMathematics at the University of Queensland, Australia.

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