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Ouvrages Année : 2019

Handbook of Mixture Analysis

Résumé

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.
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Dates et versions

hal-03943314 , version 1 (17-01-2023)

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  • HAL Id : hal-03943314 , version 1

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Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert. Handbook of Mixture Analysis. Taylor & Francis, 2019. ⟨hal-03943314⟩
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