Chapman and Hall/CRC
498 pages | 87 B/W Illus.
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time.
The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.
The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
Part I: Foundations and Methods
Introduction to Finite Mixtures - Peter J. Green
EM Methods for Finite Mixtures - Gilles Celeux
An Expansive View of EM Algorithms - David R. Hunter, Prabhani Kuruppumullage Don, and Bruce G. Lindsay
Bayesian Mixture Models: Theory and Methods - Judith Rousseau, Clara Grazian, and Jeong Eun Lee
Computational Solutions for Bayesian Inference in Mixture Models - Gilles Celeux, Kaniav Kamary, Gertraud Malsiner Walli, Jean-Michel Marin, and Christian P. Robert
Nonparametric Bayesian Mixture Models - Peter Müller
Model Selection for Mixture Models – Perspectives and Strategies - Gilles Celeux, Sylvia Frühwirth-Schnatter and Christian P. Robert
Part II: Mixture Modelling and Extensions
Model-based Clustering - Bettina Grün
Mixture Modelling of Discrete Data - Dimitris Karlis
Continuous Mixtures with Skewness and Heavy Tails - David Rossell and Mark F.J. Steel
Mixture Modelling of High-Dimensional Data - Damien McParland and Thomas Brendan Murphy
Mixtures of Experts Models - Isobel Claire Gormley and Sylvia Frühwirth-Schnatter
Hidden Markov Models in Time Series, with Applications in Economics - Sylvia Kaufmann
Mixtures of Nonparametric Components and Hidden Markov Models - Elisabeth Gassiat
Part III: Selected Applications
Applications in Industry - Kerrie Mengersen, Earl Duncan, Julyan Arbel, Clair Alston-Knox, Nicole White
Mixture Models for Image Analysis - Florence Forbes
Applications in Finance - John M. Maheu and Azam Shamsi Zamenjani
Applications in Genomics - Stéphane Robin and Christophe Ambroise
Applications in Astronomy - Michael A. Kuhn and Eric D. Feigelson