• Computer Science
  • Published in NIPS 1999

The Infinite Gaussian Mixture Model

@inproceedings{Rasmussen1999TheIG,
  title={The Infinite Gaussian Mixture Model},
  author={Carl E. Rasmussen},
  booktitle={NIPS},
  year={1999}
}
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. 

Figures and Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 718 CITATIONS

General Bayesian inference schemes in infinite mixture models

VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Infant directed speech is consistent with teaching

VIEW 17 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

A random finite set model for data clustering

  • 17th International Conference on Information Fusion (FUSION)
  • 2014
VIEW 11 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Online Fault Diagnosis in Industrial Processes Using Multimodel Exponential Discriminant Analysis Algorithm

  • IEEE Transactions on Control Systems Technology
  • 2019
VIEW 8 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

1999
2019

CITATION STATISTICS

  • 122 Highly Influenced Citations

  • Averaged 54 Citations per year from 2017 through 2019