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

Nizar Bouguila; Jian Han Wang; A. Ben Hamza

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

机译：通过有限狄利克雷混合物的似然和贝叶斯分析对软件模块进行分类

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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.

机译：在本文中，我们研究了确定性和贝叶斯方法来分析有限Dirichlet混合物。确定性方法基于似然法，而贝叶斯方法是使用Gibbs采样器实现的。两种方法的聚类数目的选择均基于贝叶斯信息准则，该准则等于最小描述长度。使用模拟数据展示了实验结果，还包括了软件模块分类的实际应用。

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《Journal of applied statistics》 |2010年第2期|235-252|共18页
作者
Nizar Bouguila; Jian Han Wang; A. Ben Hamza;
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作者单位

Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada;

Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada;

Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada;

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正文语种 eng
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关键词
dirichlet distribution; mixture modeling; maximum likelihood; EM; MDL; BIC; bayesian analysis; gibbs sampling; metropolis-hastings; software modules;

机译：狄利克雷分布混合模型;最大似然;EM;MDL;BIC;贝叶斯分析吉布斯采样大都市的狂欢;软件模块;

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1. On Bayesian Analysis Of A Finite Generalized Dirichlet Mixture Via A Metropolis-within-gibbs Sampling [J] . Nizar Bouguila, Djemel Ziou, Riad I. Hammoud Pattern Analysis and Applications . 2009,第2期

机译：吉布斯都市圈内有限广义Dirichlet混合物的贝叶斯分析
2. Variational Bayesian Inference for Infinite Dirichlet Mixture Towards Accurate Data Categorization [J] . Yuping Lai, Wenda He, Yuan Ping, Wireless personal communications: An Internaional Journal . 2018,第3期

机译：变形贝叶斯对无限Dirichlet混合物进行准确数据分类的推理
3. Prior selection method using likelihood confidence region and Dirichlet process Gaussian mixture model for Bayesian inference of building energy models [J] . Yi Dong Hyuk, Kim Deuk Woo, Park Cheol Soo Energy and Buildings . 2020,第Octa期

机译：使用似然置信区和Dirichlet工艺Gaussian推理的Dirichlet工艺Gaussian混合模型的先前选择方法
4. Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization [C] . Bromensele Samuel Oboh, Nizar Bouguila IEEE International Conference on Industrial Technology . 2017

机译：使用尺度狄利克雷分布的有限混合物无监督学习及其在软件模块分类中的应用
5. Using Dirichlet process priors for Bayesian mixture clustering [D] . Huang, Xiao 2011

机译：使用Dirichlet过程先验进行贝叶斯混合聚类
6. Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model [O] . Sang Mee Lee, Baolin Wu, John H. Kersey -1

机译：基于有限混合模型的基于基因基因富集分析方法
7. A comparison between bayesian and maximum likelihood estimations in estimating finite mixture model for financial data [O] . Seuk Yen Phoong, Mohd Tahir Ismail 2015

机译：金融数据有限混合模型中贝叶斯估计与最大似然估计的比较

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

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