A generalisation of independence in statistical models for categorical distribution

Yu Fujimoto*, Noboru Murata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this paper, generalised statistical independence in statistical models for categorical distributions is proposed from the viewpoint of generalised multiplication characterised by a monotonically increasing function and its inverse function, and it is implemented in naive Bayes models. This paper also proposes an idea of their estimation method which directly uses empirical marginal distributions to retain simplicity of calculation. This method is interpreted as an optimisation of a rough approximation of the Bregman divergence so that it is expected to have a kind of robust property. Effectiveness of proposed models is shown by numerical experiments on some benchmark datasets.

Original languageEnglish
Pages (from-to)172-187
Number of pages16
JournalInternational Journal of Data Mining, Modelling and Management
Issue number2
Publication statusPublished - 2012


  • Bregman divergence
  • Copula
  • Generalised independence
  • Independent model
  • Naive Bayes model

ASJC Scopus subject areas

  • Management Information Systems
  • Modelling and Simulation
  • Computer Science Applications


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