# Variational Bayes Approximations for Clustering via Mixtures of Normal Inverse Gaussian Distributions

arXiv: https://arxiv.org/abs/1309.1901 · HTML: https://mlsanity.com/paper/1309.1901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2013.

## Built on by (most cited first, 1 of 1)

- [Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data](https://mlsanity.com/paper/2311.07762/) (2023)

## Abstract (arXiv)

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.