Mixture: Estimation and Applications

Kerrie L. Mengersen, Queensland University of Technology, Australia
Christian P. Robert, Université Paris-Dauphine, France
D. Michael Titterington, University of Glasgow, UK

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.