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

Author

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  • Nizar Bouguila
  • Jian Han Wang
  • A. Ben Hamza

Abstract

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.

Suggested Citation

  • Nizar Bouguila & Jian Han Wang & A. Ben Hamza, 2010. "Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 235-252.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:235-252
    DOI: 10.1080/02664760802684185
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    Cited by:

    1. Wentao Fan & Nizar Bouguila, 2013. "Infinite Dirichlet mixture models learning via expectation propagation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(4), pages 465-489, December.
    2. Sukhdev Singh & Reza Arabi Belaghi & Mehri Noori Asl, 2019. "Estimation and prediction using classical and Bayesian approaches for Burr III model under progressive type-I hybrid censoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 746-764, August.

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