 A generalisation of independence in statistical models for categorical distributionYu Fujimoto and Noboru Murata International Journal of Data Mining, Modelling and Management, 2012, vol. 4, issue 2, 172-187 Abstract: 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. Keywords: independent models; naive models; Bayes models; Thomas Bayes; generalised independence; copulas; Bregman divergence; statistical models; categorical distribution; statistical independence; generalised multiplication; monotonic increases; inverse functions; estimation methods; empirical distributions; marginal distributions; simplicity; calculations; robust properties; benchmark datasets; data mining; data modelling; data management; intelligent data analysis. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:4:y:2012:i:2:p:172-187 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.