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--- seminars:stat:181025 [2018/10/24 17:59] – created qyu
+++ seminars:stat:181025 [2018/10/25 11:33] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, October 25, 2018 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Fang Yuan, Binghamton University |
+^  **TITLE:**|Dirichlet Process Mixtures of Multivariate Normal-Inverse Gaussian Distributions  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+An expectation-maximization framework for clustering using
+finite mixture models can sometimes yield uncertainty in deciding the
+number of clusters. A Dirichlet process mixture model can alleviate
+this difficulty of finding the correct number of mixture components by
+inferring the number of clusters directly in a Bayesian framework. In
+this talk, I will discuss the Dirichlet process as well as the general
+framework for Dirichlet process mixture models. Implementation of a
+Dirichlet process mixture of Gaussian distributions will be presented
+and the generalization of this to a Dirichlet process mixture of
+Multivariate Normal Inverse Gaussian (MNIG) distribution will be
+discussed in detail. An algorithm for clustering skewed data based on
+a Dirichlet process mixture of MNIG distributions will be discussed.
+</WRAP>