The Infinite Gaussian Mixture Model
Abstract
In a Bayesian mixture model it is not necessary a priori to limit the num(cid:173) ber of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of find(cid:173) ing 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.
Cite
Text
Rasmussen. "The Infinite Gaussian Mixture Model." Neural Information Processing Systems, 1999.Markdown
[Rasmussen. "The Infinite Gaussian Mixture Model." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/rasmussen1999neurips-infinite/)BibTeX
@inproceedings{rasmussen1999neurips-infinite,
title = {{The Infinite Gaussian Mixture Model}},
author = {Rasmussen, Carl Edward},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {554-560},
url = {https://mlanthology.org/neurips/1999/rasmussen1999neurips-infinite/}
}