Handbook of Mixture Analysis
Frühwirth-Schnatter, Sylvia; Celeux, Gilles; Robert, Christian P. (2019), Handbook of Mixture Analysis, Taylor & Francis. 10.1201/9780429055911
Taylor & Francis
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Vienna University of Economics and Business
Inria Saclay - Ile de France
Robert, Christian P.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)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.
Subjects / KeywordsEngineering & Technology; Mathematics & Statistics
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Some discussions on the Read Paper Beyond subjective and objective in statistics" by A. Gelman and C. Hennig" Celeux, Gilles; Jewson, Jack; Josse, Julie; Marin, Jean-Michel; Robert, Christian P. (2017) Document de travail / Working paper