Mixtures

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.