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  The Infinite Gaussian Mixture Model

Rasmussen, C. (2000). The Infinite Gaussian Mixture Model. Advances in Neural Information Processing Systems 12, 554-560.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E4CC-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E4CD-A
Genre: Conference Paper

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 Creators:
Rasmussen, CE1, Author           
Solla, Editor
S.A., Editor
Leen, T.K., Editor
Müller, K-R, Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-11245-0
URI: http://books.nips.cc/nips12.html
BibTex Citekey: 2299
 Degree: -

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Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
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Title: Advances in Neural Information Processing Systems 12
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 554 - 560 Identifier: -