FINITE MIXTURE MODELS MCLACHLAN PDF

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View Table of Contents for Finite Mixture Models Geoffrey McLachlan · David Peel account of the theory and applications of modeling via finite mixture distributions. With an Summary · PDF · Request permissions · xml. By Geoffrey McLachlan and David Peel. Copyright DOWNLOAD PDF. Finite Finite Mixture and Markov Switching Models (Springer Series in Statistics). John Wiley, p. The importance of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on.


Finite Mixture Models Mclachlan Pdf

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different cluster. In addition to clustering purposes, finite mixtures of distri- To this extent finite mixture models have continued to receive increasing attention from .. The reader is referred to McLachlan and Peel. () for a. Abstract: Finite mixture models are being increasingly used to model the distributions of a ization of the mixture probability density function (p.d.f.),. = (о ; y)м g. Request PDF on ResearchGate | On Jan 1, , Geoffrey J. McLachlan and others published Finite Mixture Model.

Bounded variable logics and counting: A study in finite models.

Concrete mixture proportioning: Finite Mathematics. Finite Elemente. Finite mathematics.

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Finite Fields Theory of Finite Groups. Enumeration of Finite Groups. Print ISBN: Book Series: Wiley Series in Probability and Statistics. About this book 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.

1 Introduction

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: Reviews "This is an excellent book I enjoyed reading this book. The emphasis is on the applications of mixture models, not only in mainstream statistical analyses.

With the advent of inexpensive, high-speed computers and the simultaneous rapid development in posterior simulation techniques such as Markov chain Monte Carlo MCMC methods for enabling Bayesian estimation to be undertaken, practitioners are increasingly turning to Bayesian methods for the analysis of complicated statistical models. In this book, we consider the latest developments in Bayesian estimation of mixture models.

New topics that are covered in this book include the scaling of the EM algorithm to allow mixture models to be used in data mining applications involving massively huge databases. Another topic concerns the use of hierarchical mixtures-of-experts models as a powerful new approach to nonlinear regression that is a serious competitor to well-known statistical procedures such as MARS and CART.

Other recent developments covered include the use of mixture models for handling overdispersion in generalized linear models and proposals for dealing with mixed continuous and categorical variables.

In other recent work, there is the proposal to use t components in the mixture model to provide a robust approach to mixture modeling. A further topic is the use of mixtures of factor analyzers which provide a way of fitting mixture models to high-dimensional data.

As mixture models provide a convenient basis for the modeling of dependent data by allowing the component-indicator variables to have a Markovian structure, there is also coverage of the latest developments in hidden Markov models, including the Bayesian approach to this problem. Another problem considered is the fitting of mixture models to multivariate data in binned form, which arises in some important medical applications in practice.

Finite mixture models

The book also covers the latest developments on existing issues with mixture modeling, such as assessing the number of components to be used in a mixture model and the associated problem of determining how many clusters there are in clustering applications with mixture models.

In presenting these latest results, the authors have attempted to draw together the statistical literature with the machine learning and pattern recognition literature.Google Scholar Hawkins D.

Concrete mixture proportioning: As an application of the proposed EM algorithm, a realistic adaptive design clinical trial example appears in Section 5. 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.

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. Time confounding can occur when medical equipment, concomitant medications, staff, and even disease severity of patients entering the trial at different times changes and patients are not allocated to the two treatments in some sort of balanced manner.

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