Mixture Models With a Prior on the Number of Components

A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components-that is, to use a mixture of finite mixtures (MFM). The most commonly used method of inference f...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 113; H. 521; S. 340 - 356
Hauptverfasser:	Miller, Jeffrey W., Harrison, Matthew T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 02.01.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	Bayesian Bayesian analysis Bayesian theory Cancer Clustering Computer simulation Density estimation Dirichlet problem Gene expression Inference Markov analysis Markov chain Markov chains Mixtures Model selection Monte Carlo method Monte Carlo simulation neoplasms Nonparametric Nonparametric statistics Partition Probabilistic models Property Regression analysis Representations Restaurants Reversible Samplers Statistical methods statistical models Statistics Subtypes Theory and Methods model selection clustering density estimation nonparametric Bayesian
ISSN:	0162-1459, 1537-274X, 1537-274X
Online-Zugang:	Volltext
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