Mixtures

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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.