Handbook of mixture analysis - Boca Raton CRC Press 2019 - xxiii, 497p. With index - Chapman & Hall/CRC handbooks of modern statistical methods .

Table of Contents

Part I: Foundations and Methods
1 Introduction to Finite Mixtures - Peter J. Green
2 EM Methods for Finite Mixtures - Gilles Celeux
3 An Expansive View of EM Algorithms - David R. Hunter, Prabhani Kuruppumullage Don, and Bruce G. Lindsay
4 Bayesian Mixture Models: Theory and Methods - Judith Rousseau, Clara Grazian, and Jeong Eun Lee
5 Computational Solutions for Bayesian Inference in Mixture Models - Gilles Celeux, Kaniav Kamary, Gertraud Malsiner Walli, Jean-Michel Marin, and Christian P. Robert
6 Nonparametric Bayesian Mixture Models - Peter Müller
7 Model Selection for Mixture Models – Perspectives and Strategies - Gilles Celeux, Sylvia Frühwirth-Schnatter and Christian P. Robert

Part II: Mixture Modelling and Extensions
8 Model-based Clustering - Bettina Grün
9 Mixture Modelling of Discrete Data - Dimitris Karlis
10 Continuous Mixtures with Skewness and Heavy Tails - David Rossell and Mark F.J. Steel
11 Mixture Modelling of High-Dimensional Data - Damien McParland and Thomas Brendan Murphy
12 Mixtures of Experts Models - Isobel Claire Gormley and Sylvia Frühwirth-Schnatter
13 Hidden Markov Models in Time Series, with Applications in Economics - Sylvia Kaufmann
14 Mixtures of Nonparametric Components and Hidden Markov Models - Elisabeth Gassiat

Part III: Selected Applications
15 Applications in Industry - Kerrie Mengersen, Earl Duncan, Julyan Arbel, Clair Alston-Knox, Nicole White
16 Mixture Models for Image Analysis - Florence Forbes
17 Applications in Finance - John M. Maheu and Azam Shamsi Zamenjani
18 Applications in Genomics - Stéphane Robin and Christophe Ambroise
19 Applications in Astronomy - Michael A. Kuhn and Eric D. Feigelson

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. Features:
Provides a comprehensive overview of the methods and applications of mixture modelling and analysis
Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications
Contains many worked examples using real data, together with computational implementation, to illustrate the methods described
Includes contributions from the leading researchers in the field
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.



Mixture distributions - Probability theory
Distribution - Probability theory
Statistical theory and methods
Machine learning

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