Handbook of Mixture Analysis

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...

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Hauptverfasser:	Frühwirth-Schnatter, Sylvia, Celeux, Gilles, Robert, Christian P.
Format:	E-Book Buch
Sprache:	Englisch
Veröffentlicht:	Boca Raton CRC Press 2019 CRC Press LLC Chapman & Hall
Ausgabe:	1
Schriftenreihe:	Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Schlagworte:	Bayesian inference clustering COMPUTERSCIENCEnetBASE Distribution (Probability theory) EM algorithm hidden markov models image analysis INFORMATIONSCIENCEnetBASE Machine Learning Mathematics Mixture distributions (Probability theory) mixture models SCI-TECHnetBASE Statistical Computing Statistical Theory & Methods Statistics STATSnetBASE STMnetBASE HMRFs MCMC Algorithm Posterior Distribution Data Sets Component Specific Parameters Location Scale Mixture Bayesian Nonparametric Modelling Infinite Mixture Model Standard Em Algorithm DP Finite Mixture Models Dirichlet Process Mixture Model DP Mixture Posterior Draws MS Model Em Algorithm Em Type Algorithm MCMC Sampling HMMs Latent Allocation Variables Finite Mixture Nonparametric Mixture Models Mixture Model Normal Mixture Models Hidden Markov Random Field Model
ISBN:	9781498763813, 1498763812, 9780367732066, 0367732068
Online-Zugang:	Volltext
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