A generalisation of independence in statistical models for categorical distribution
by Yu Fujimoto; Noboru Murata
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 4, No. 2, 2012

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.

Online publication date: Sat, 23-Aug-2014

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