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

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.

• Publication year

  2013

• Venue

  Advances in Data Analysis and Classification

• Publication date

  2013-09-07

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/s11634-014-0165-7arXiv 1309.1901
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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