A New Finite Approximation for the NGG Mixture Model: An Application to Density Estimation

被引：0

作者：

Bianchini, Ilaria ^{[1
,2
]}

机构：

[1] Politecn Milan, Dept Math, I-20133 Milan, Italy

[2] CNR, Ist Matemat Appl & Tecnol Informat IMATI, Milan, Italy

来源：

BAYESIAN STATISTICS FROM METHODS TO MODELS AND APPLICATIONS: RESEARCH FROM BAYSM 2014 | 2015年 / 126卷

关键词：

Bayesian nonparametric mixture models; A-priori truncation method; Normalized generalized gamma process; SAMPLING METHODS; DIRICHLET; INFERENCE;

D O I：

10.1007/978-3-319-16238-6_2

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

A new class of random probability measures, approximating the well-known normalized generalized gamma (NGG) process, is defined. The new process is built from the representation of the NGG process as a discrete measure, where the weights are obtained by normalization of points of a Poisson process larger than a threshold epsilon. Consequently, the new process has an as surely finite number of location points. This process is then considered as the mixing measure in a mixture model for density estimation; we apply it to the popular Galaxy dataset. Moreover, we perform some robustness analysis to investigate the effect of the choice of the hyperparameters.

引用

页码：15 / 26

页数：12

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