**Problem of the Week**

**Math Club**

**BUGCAT**

**Zassenhaus Conference**

**Hilton Memorial Lecture**

**BingAWM**

seminars:stat:181025

Statistics Seminar

Department of Mathematical Sciences

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 |

**Abstract**

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.

seminars/stat/181025.txt · Last modified: 2018/10/25 07:33 by qyu

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 3.0 Unported