English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 DetailsSummary
  The Infinite Gaussian Mixture Model

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

Item is

 

show all hide all

Basic

show hide
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

Files

show Files

Locators

show

Creators

show
hide
 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              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 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: -

Event

show
hide
Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Advances in Neural Information Processing Systems 12
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 554 - 560 Identifier: -