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: |
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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}, doi = {}, month_numeric = {6} } |