The infinite Gaussian mixture model

被引：0

作者：

Rasmussen, CE ^{[1
]}

机构：

[1] Tech Univ Denmark, Dept Math Modelling, DK-2800 Lyngby, Denmark

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 12 | 2000年 / 12卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：554 / 560

页数：7

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