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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 2/2014

01.06.2014 | Regular Article

Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions

verfasst von: Sanjeena Subedi, Paul D. McNicholas

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 2/2014

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Abstract

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a substantial departure from the traditional EM approach and alleviates some of the associated computational complexities and uncertainties. Our variational algorithm is applied to simulated and real data. The paper concludes with discussion and suggestions for future work.
Vorheriger Artikel Graphical tools for model-based mixture discriminant analysis
Nächster Artikel Threshold optimization for classification in imbalanced data in a problem of gamma-ray astronomy

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Metadaten
Titel
Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions
verfasst von
Sanjeena Subedi
Paul D. McNicholas
Publikationsdatum
01.06.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 2/2014
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-014-0165-7

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