Author(s): Kerrie L. Mengersen (Author), Christian Robert (Author), Mike Titterington (Author)
Publisher: Wiley
Publication Date: 6 May 2011
Edition: 1st
Language: English
Print length: 330 pages
ISBN-10: 111999389X
ISBN-13: 9781119993896
Book Description
This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete.
The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subject. The applications are drawn from scientific discipline, including biostatistics, computer science, ecology and finance. This area of statistics is important to a range of disciplines, and its methodology attracts interest from researchers in the fields in which it can be applied.
Editorial Reviews
From the Inside Flap
Research on inference and computational techniques for mixture-type models is experiencing new and major advances and the call to mixture modelling in various science and business areas is omnipresent.
Mixtures: Estimation and Applications contains a collection of chapters written by international experts in the field, representing the state of the art in mixture modelling, inference and computation. A wide and representative array of applications of mixtures, for instance in biology and economics, are covered. Both Bayesian and non-Bayesian methodologies, parametric and non-parametric perspectives, statistics and machine learning schools appear in the book.
This book:
Provides a contemporary account of mixture inference, with Bayesian, non-parametric and learning interpretations.
Explores recent developments about the EM (expectation maximization) algorithm for maximum likelihood estimation.
Looks at the online algorithms used to process unlimited amounts of data as well as large dataset applications.
Compares testing methodologies and details asymptotics in finite mixture models.
Introduces mixture of experts modeling and mixed membership models with social science applications.
Addresses exact Bayesian analysis, the label switching debate, and manifold Markov Chain Monte Carlo for mixtures.
Includes coverage of classification and machine learning extensions.
Features contributions from leading statisticians and computer scientists.
This area of statistics is important to a range of disciplines, including bioinformatics, computer science, ecology, social sciences, signal processing, and finance. This collection will prove useful to active researchers and practitioners in these areas.
From the Back Cover
Research on inference and computational techniques for mixture-type models is experiencing new and major advances and the call to mixture modelling in various science and business areas is omnipresent.
Mixtures: Estimation and Applications contains a collection of chapters written by international experts in the field, representing the state of the art in mixture modelling, inference and computation. A wide and representative array of applications of mixtures, for instance in biology and economics, are covered. Both Bayesian and non-Bayesian methodologies, parametric and non-parametric perspectives, statistics and machine learning schools appear in the book.
This book:
Provides a contemporary account of mixture inference, with Bayesian, non-parametric and learning interpretations.
Explores recent developments about the EM (expectation maximization) algorithm for maximum likelihood estimation.
Looks at the online algorithms used to process unlimited amounts of data as well as large dataset applications.
Compares testing methodologies and details asymptotics in finite mixture models.
Introduces mixture of experts modeling and mixed membership models with social science applications.
Addresses exact Bayesian analysis, the label switching debate, and manifold Markov Chain Monte Carlo for mixtures.
Includes coverage of classification and machine learning extensions.
Features contributions from leading statisticians and computer scientists.
This area of statistics is important to a range of disciplines, including bioinformatics, computer science, ecology, social sciences, signal processing, and finance. This collection will prove useful to active researchers and practitioners in these areas.
About the Author
Kerrie L. Mengersen, Queensland University of Technology, Australia.
Christian P. Robert, Universite Paris-Dauphine, France.
D. Michael Titterington, University of Glasgow, Scotland.
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