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.