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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, Month 31, 2017 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Yuan Fang, Binghamton University |
+^  **TITLE:**|Bayesian Approach to Parameter Estimation   |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+Bayesian Approach to Parameter Estimation and Clustering for
+the Mixtures of Multivariate Normal Inverse Gaussian Distributions
+Abstract: Increasingly, non-Gaussian mixture models are gaining
+attention for mixture model-based clustering particularly when dealing
+with data that exhibit features such as skewness and heavy tails. One
+such mixture distribution is the mixtures of multivariate normal
+inverse Gaussian (MNIG) distribution. MNIG 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
+their mixture, which makes them suitable for a wide range of datasets.
+In this talk, I will focus on an approach for parameter estimation of
+mixtures of MNIG distributions in a Bayesian framework via a Gibbs
+scheme. Novel approaches to simulate univariate generalized inverse
+Gaussian (GIG) random variables and matrix generalized inverse
+Gaussian (MGIG) random matrices will be provided. The proposed
+algorithm will be applied to both simulated and real data. Some future
+work on extending  finite mixture of MNIG distributions to an infinite
+mixture model framework will also be discussed.
+</WRAP>