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.


  • 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.

part I|1 pages

Foundations and Methods

chapter 1|18 pages

Introduction to Finite Mixtures

ByPeter J. Green

chapter 2|19 pages

EM Methods for Finite Mixtures

ByGilles Celeux

chapter 3|12 pages

An Expansive View of EM Algorithms

ByDavid R. Hunter, Prabhani Kuruppumullage Don, Bruce G. Lindsay

chapter 4|20 pages

Bayesian Mixture Models: Theory and Methods

ByJudith Rousseau, Clara Grazian, Jeong Eun Lee

chapter 5|24 pages

Computational Solutions for Bayesian Inference in Mixture Models

ByGilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli, Jean-Michel Marin, Christian P. Robert

chapter 6|20 pages

Bayesian Nonparametric Mixture Models

ByPeter Müller

chapter 7|38 pages

Model Selection for Mixture Models – Perspectives and Strategies

ByGilles Celeux, Sylvia Frühwirth-Schnatter, Christian P. Robert

part II|1 pages

Mixture Modelling and Extensions

chapter 8|36 pages

Model-Based Clustering

ByBettina Grün

chapter 9|26 pages

Mixture Modelling of Discrete Data

ByDimitris Karlis

chapter 10|19 pages

Continuous Mixtures with Skewness and Heavy Tails

ByDavid Rossell, Mark F. J. Steel

chapter 11|32 pages

Mixture Modelling of High-Dimensional Data

ByDamien McParland, Thomas Brendan Murphy

chapter 12|37 pages

Mixture of Experts Models

ByIsobel Claire Gormley, Sylvia Frühwirth-Schnatter

chapter 13|33 pages

Hidden Markov Models in Time Series, with Applications in Economics

BySylvia Kaufmann

chapter 14|18 pages

Mixtures of Nonparametric Components and Hidden Markov Models

ByElisabeth Gassiat

part III|1 pages

Selected Applications

chapter 15|21 pages

Applications in Industry

ByKerrie Mengersen, Earl Duncan, Julyan Arbel, Clair Alston-Knox, Nicole White

chapter 16|21 pages

Mixture Models for Image Analysis

ByFlorence Forbes

chapter 17|31 pages

Applications in Finance

ByJohn M. Maheu, Azam Shamsi Zamenjani

chapter 18|23 pages

Applications in Genomics

ByStéphane Robin, Christophe Ambroise

chapter 19|27 pages

Applications in Astronomy

ByMichael A. Kuhn, Eric D. Feigelson