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Activity Number: 404 - Bayesian Clustering and Classification
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #324009 View Presentation
Title: Bayesian Estimation for the Multivariate Normal Inverse Gaussian Model
Author(s): YUAN FANG* and Sanjeena Dang and Dimitris Karlis
Companies: Binghamton University and Binghamton University and Athens University of Economics and Business
Keywords: Model-based Clustering ; Multivariate Normal Inverse Gaussian ; Bayesian Analysis
Abstract:

In recent days, non-Gaussian mixture models are increasingly gaining attention for mixture model-based clustering. One such distribution is the multivariate normal inverse Gaussian (MNIG) distribution. It arises from a mean-variance mixture of a multivariate Gaussian distribution with the inverse Gaussian distribution. A mixture of MNIG distributions has the flexibility to represent both skewed and symmetric clusters as well as a mixture thereof, which makes them suitable for a wide range of datasets. In this talk, we will focus on a Bayesian approach via a Gibbs scheme for parameter estimation for a mixture of MNIG distributions. We will also discuss a novel approach to simulate matrix generalized inverse Gaussian (MGIG) random matrices. Our algorithm will be illustrated on both toy and benchmark data.


Authors who are presenting talks have a * after their name.

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