Dirichlet process mixture models with multiple modalities

Published

Journal Article

The Dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. The set of parameters over which it is defined is generally used for a single, parametric distribution. We extend this idea to parameter spaces that characterize multiple distributions, or modalities. In this framework, observations containing multiple, incompatible pieces of information can be mixed upon, allowing for all information to inform the final clustering result. We provide a general MCMC sampling scheme and demonstrate this framework on a Gaussian-HMM mixture model applied to synthetic and Major League Baseball data. ©2009 IEEE.

Full Text

Duke Authors

Cited Authors

  • Paisley, J; Carin, L

Published Date

  • September 23, 2009

Published In

Start / End Page

  • 1613 - 1616

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2009.4959908

Citation Source

  • Scopus