Header logo is ei

The Infinite Gaussian Mixture Model

2000

Conference Paper

ei


In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the ``right'' number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.

Author(s): Rasmussen, CE.
Book Title: Advances in Neural Information Processing Systems 12
Journal: Advances in Neural Information Processing Systems 12
Pages: 554-560
Year: 2000
Month: June
Day: 0
Editors: Solla, S.A. , T.K. Leen, K-R M{\"u}ller
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Event Place: Denver, CO, USA

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-11245-0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{2299,
  title = {The Infinite Gaussian Mixture Model},
  author = {Rasmussen, CE.},
  journal = {Advances in Neural Information Processing Systems 12},
  booktitle = {Advances in Neural Information Processing Systems 12},
  pages = {554-560},
  editors = {Solla, S.A. , T.K. Leen, K-R M{\"u}ller},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2000},
  month_numeric = {6}
}