Handbook of Mixture Analysis

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 Muller Model Selection for Mixture Models - Perspectives and Strategies - Gilles Celeux, Sylvia Fruhwirth-Schnatter and Christian P. Robert Part II: Mixture Modelling and Extensions Model-based Clustering - Bettina Grun 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 Fruhwirth-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 - Stephane Robin and Christophe Ambroise Applications in Astronomy - Michael A. Kuhn and Eric D. Feigelson

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