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  The Infinite Gaussian Mixture Model

Rasmussen, C. (2000). The Infinite Gaussian Mixture Model. In S. Solla, T. Leen, & K. Müller (Eds.), Advances in Neural Information Processing Systems 12 (pp. 554-560). Cambridge, MA, USA: MIT Press.

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 Creators:
Rasmussen, CE1, Author              
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1External Organizations, ou_persistent22              

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 Abstract: 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.

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 Dates: 2000-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2299
 Degree: -

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Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
Start-/End Date: 2000-11-29 - 2000-12-04

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Title: Advances in Neural Information Processing Systems 12
Source Genre: Proceedings
 Creator(s):
Solla, SA, Editor
Leen, TK, Editor
Müller, K, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 554 - 560 Identifier: ISBN: 0-262-11245-0