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Jeffreys prior for mixture models [PDF]

, 2014
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper.
GRAZIAN, CLARA, C. P. Robert
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Mixture Models

2008
Bruce G. Lindsay, Michael Stewart
openaire   +1 more source

Mixture Models

2017
Vermunt, J.K., Paas, L.J.
openaire   +2 more sources

A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method

IEEE Access, 2021
Maruf Gogebakan
exaly  

Finite mixture models: McLachlan/finite mixture models

2000
An up-to-date, comprehensive account of major issues in finite mixture modeling This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech ...
McLachlan, Geoffrey, Peel, David
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Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
M A T Figueiredo, A K Jain
exaly  

Mixture Model (Finite Mixture Model)

2004
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Finite Mixture Models

Technometrics, 2002
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Assessing a Mixture Model for Clustering With the Integrated Completed Likelihood

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
G Govaert
exaly  

Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Paul D. McNicholas, Ryan P. Browne
exaly