Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures

被引：13

作者：

Bouguila, Nizar ^{[1
]}

Wang, Jian Han ^{[1
]}

Hamza, A. Ben ^{[1
]}

机构：

[1] Concordia Univ, Concordia Inst Informat Syst Engn, Montreal, PQ, Canada

来源：

JOURNAL OF APPLIED STATISTICS | 2010年 / 37卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

dirichlet distribution; mixture modeling; maximum likelihood; EM; MDL; BIC; Bayesian analysis; Gibbs sampling; Metropolis-Hastings; software modules; MODEL;

D O I：

10.1080/02664760802684185

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, we examine deterministic and Bayesian methods for analyzing finite Dirichlet mixtures. The deterministic method is based on the likelihood approach, and the Bayesian approach is implemented using the Gibbs sampler. The selection of the number of clusters for both approaches is based on the Bayesian information criterion, which is equivalent to the minimum description length. Experimental results are presented using simulated data, and a real application for software modules classification is also included.

引用

页码：235 / 252

页数：18

共 28 条

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